Table of Contents
- 1 Objetivos
- 2 KPI
- 3 Datos
- 4 Análisis Exploratorio (EDA)
- 4.1 Target Variable
- 4.2 Variables Independientes
- 4.2.1 Secondary Applicant
- 4.2.2 Caracteristicas del Préstamo
- 4.2.3 Caracteristicas del Prestatario
- 4.2.4 Evaluación del Prestatario
- 4.2.5 Historial Crediticio del Prestatario
- 4.2.5.1 Deliquency in last 2 years
- 4.2.5.2 Inquires Last 6 Months
- 4.2.5.3 Number of derogatory public records
- 4.2.5.4 Months since last deliquency
- 4.2.5.5 Months since last record
- 4.2.5.6 Months since last major derogative
- 4.2.5.7 Collections 12 Months (Exclude Medical Credits)
- 4.2.5.8 Total collection amount
- 4.2.5.9 Accounts open past 24 months
- 4.2.5.10 Chargeoff within 12 months
- 4.2.5.11 Months since old Credit Account
- 4.2.5.12 Months since old opened revolvent account
- 4.2.5.13 Months since recent opened account
- 4.2.5.14 Number of accounts ever 120 or more days past due
- 4.2.5.15 Number of installment accounts
- 4.2.5.16 Number of accounts currently 120 days past due (updated in past 2 months)
- 4.2.5.17 Number of accounts currently 30 days past due (updated in past 2 months)
- 4.2.5.18 Number of accounts 90 or more days past due in last 24 months
- 4.2.5.19 Number of accounts opened in past 12 months
- 4.2.5.20 Percent of trades never delinquent
- 4.2.5.21 Percentage of all bankcard accounts > 75% of limit.
- 4.2.5.22 Number of public record bankruptcies
- 4.2.6 Endeudamiento del Prestatario
- 4.2.7 Caracteristicas Financieras Actuales
- 4.2.7.1 Open Accounts (Open Acc)
- 4.2.7.2 Revolving Balance (Revol Acc)
- 4.2.7.3 Revolving line utilization (Revol Util)
- 4.2.7.4 Total Number of Credit Lines (Total Acc)
- 4.2.7.5 Number of satisfactory accounts (Num sats)
- 4.2.7.6 The number of accounts on which the borrower is now delinquent. (Acc Now Delinq)
- 4.2.7.7 Total current balance of all accounts (Total Cur Bal)
- 4.2.7.8 Total current balance of all installment accounts (Total Bal Il)
- 4.2.7.9 Ratio of total current balance to high credit/credit limit on all install acct (Il Util)
- 4.2.7.10 Maximum current balance owed on all revolving accounts (Max Bal BC)
- 4.2.7.11 Balance to credit limit on all trades (All Util)
- 4.2.7.12 Total revolving high Limit (Total Rev Hi Lim)
- 4.2.7.13 Number of finance trades (Total Cu Tl)
- 4.2.7.14 Average current balance of all accounts (Avg Cur Bal)
- 4.2.7.15 Total open to buy on revolving bankcards. (Bc Open To Buy)
- 4.2.7.16 Ratio of total current balance to high credit/credit limit for all bankcard accounts. (Bc Util)
- 4.2.7.17 The past-due amount owed for the accounts on which the borrower is now delinquent. (Delinq Amnt)
- 4.2.7.18 Mortgage accounts
- 4.2.7.19 Number of mortgage accounts. (Mort Acc)
- 4.2.8 Indicadores de Crédito
- 4.2.9 Rendimiento del Préstamo
- 4.2.10 'Months since'
- 4.2.11 Hardship plan
- 4.2.12 Disbursement method
- 4.2.13 Settlement
- 4.3 Revisión del trabajo realizado.
- 5 Selección de Variables
Práctica Final¶
- Curso: Machine Learning
- Integrantes:
- Marina Ortín
- Mayra Goicochea
In [2]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib import rcParams
%matplotlib inline
Objetivos¶
El objetivo de este ejercicio es el desarrollo de un modelo que permita la identificación de prestamistas que puedan realizar un impago en el crédito concedido. En este sentido, probaremos varios modelos, desde regresión logística a XGBoost, con el objetivo de encontrar el modelo óptimo. En este sentido, nos remitimos al apartado "KPI", en el que definimos los indicadores de desempeño que guiarán nuestra decisión.
Para ello, es necesario analizar qué variables son determinantes para la concesión del crédito. Este estudio se puede encontrar en el epígrafe "Análisis exploratorio (EDA)".
KPI¶
En este apartado describiremos brevemente los indicadores de performance que utilizaremos para elegir el mejor modelo.
Aunque nos basaremos en la medida de accuracy, tomaremos otras medidas adicionales, como la matriz de confusión, área bajo la curva (AUC) y F1 Score.
Definimos accuracy como el ratio entre el número de predicciones correctas realizadas por el modelo y el número total de observaciones input. Se trata de la medida básica que utilizamos para la selección de modelos; sin embargo, realizaremos cálculos adicionales como:
Matriz de confusión: se trata de una matriz que describe, de forma completa, el desempeño del modelo. De esta forma, se puede observar en la diagonal principal el número de aciertos del modelo, así como los falsos negativos y falsos positivos que ha devuelto.
Área bajo la curva (AUC), curva ROC: esta medida es ampliamente utilizada en problemas de clasificación binaria, como este. Se trata de una representación gráfica que, dado un umbral de clasificación, representa sensibilidad y especificidad del modelo.
F1 Score: se trata de una medida de la accuracy. Matemáticamente, se calcula como la media armónica entre precisión y recall. Cabe recordar, en este sentido, que la precisión se define como el número de positivos acertados sobre el número de aciertos total.
$Precision = \frac{TruePositives}{TruePositives + FalsePositives}$
Mientras que el recall es el número de verdaderos positivos sobre el total de muestras que deberían haberse identificado como positivas.
$Recall = \frac{TruePositives}{TruePositives + FalseNegatives}$
Datos¶
Mediante revisión previa en el archivo del dataset, se observa que tiene 2.260.668 registros almacenados con 145 columnas. La columna id, member_id y url no tienen propiedad discriminante para el análisis de concesión de créditos es así que no serán consideradas en el dataset final. También se muestra que se tiene una columna loan_status de la cual se considerará sólo las categorías "Default","Charged Off" y "Fully Paid" que se trata de préstamos históricos que tomaremos de referencia para detectar las dos clases "Default" (préstamos impagos) y "No Default" (préstamos pagados). Dada éstas condiciones se procede a la carga del pandas dataframe loan con estas restricciones:
In [3]:
chunksize = 1000
status_set = ['Fully Paid',
'Charged Off',
'Does not meet the credit policy. Status:Fully Paid',
'Does not meet the credit policy. Status:Charged Off',
'Default']
all_loans = pd.concat([chunk[chunk['loan_status'].isin(status_set)]
for chunk in pd.read_csv('../data/loan.csv', chunksize=chunksize)])
In [4]:
loan =all_loans.drop(['id','member_id','url'],1)
In [5]:
loan.info()
loan.describe()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1306387 entries, 100 to 2260664 Columns: 142 entries, loan_amnt to settlement_term dtypes: float64(102), int64(4), object(36) memory usage: 1.4+ GB
Out[5]:
| loan_amnt | funded_amnt | funded_amnt_inv | int_rate | installment | annual_inc | dti | delinq_2yrs | inq_last_6mths | mths_since_last_delinq | ... | deferral_term | hardship_amount | hardship_length | hardship_dpd | orig_projected_additional_accrued_interest | hardship_payoff_balance_amount | hardship_last_payment_amount | settlement_amount | settlement_percentage | settlement_term | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1.306387e+06 | 1.306387e+06 | 1.306387e+06 | 1.306387e+06 | 1.306387e+06 | 1.306383e+06 | 1.306075e+06 | 1.306358e+06 | 1.306357e+06 | 647355.000000 | ... | 5335.0 | 5335.000000 | 5335.0 | 5335.000000 | 3433.000000 | 5335.000000 | 5335.000000 | 32003.000000 | 32003.000000 | 32003.000000 |
| mean | 1.440552e+04 | 1.439643e+04 | 1.436810e+04 | 1.325918e+01 | 4.377787e+02 | 7.614879e+04 | 1.825024e+01 | 3.174559e-01 | 6.664786e-01 | 34.303021 | ... | 3.0 | 148.212679 | 3.0 | 13.968510 | 414.084599 | 11028.726163 | 184.578017 | 5051.677568 | 47.669719 | 13.088429 |
| std | 8.698613e+03 | 8.694589e+03 | 8.700952e+03 | 4.757423e+00 | 2.610471e+02 | 7.004582e+04 | 1.093374e+01 | 8.770225e-01 | 9.640143e-01 | 21.931669 | ... | 0.0 | 129.101351 | 0.0 | 9.766266 | 360.416982 | 7466.642417 | 195.413041 | 3687.945922 | 7.326161 | 8.281600 |
| min | 5.000000e+02 | 5.000000e+02 | 0.000000e+00 | 5.310000e+00 | 4.930000e+00 | 0.000000e+00 | -1.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000 | ... | 3.0 | 0.640000 | 3.0 | 0.000000 | 1.920000 | 55.730000 | 0.010000 | 44.210000 | 0.200000 | 0.000000 |
| 25% | 8.000000e+03 | 8.000000e+03 | 7.825000e+03 | 9.750000e+00 | 2.484800e+02 | 4.576000e+04 | 1.178000e+01 | 0.000000e+00 | 0.000000e+00 | 16.000000 | ... | 3.0 | 53.725000 | 3.0 | 6.000000 | 147.780000 | 5101.920000 | 39.785000 | 2243.100000 | 45.000000 | 6.000000 |
| 50% | 1.200000e+04 | 1.200000e+04 | 1.200000e+04 | 1.274000e+01 | 3.754300e+02 | 6.500000e+04 | 1.760000e+01 | 0.000000e+00 | 0.000000e+00 | 31.000000 | ... | 3.0 | 110.650000 | 3.0 | 15.000000 | 307.650000 | 9346.010000 | 121.350000 | 4200.000000 | 45.000000 | 14.000000 |
| 75% | 2.000000e+04 | 2.000000e+04 | 2.000000e+04 | 1.599000e+01 | 5.799100e+02 | 9.000000e+04 | 2.403000e+01 | 0.000000e+00 | 1.000000e+00 | 50.000000 | ... | 3.0 | 204.660000 | 3.0 | 23.000000 | 572.130000 | 15361.420000 | 267.470000 | 6905.590000 | 50.000000 | 18.000000 |
| max | 4.000000e+04 | 4.000000e+04 | 4.000000e+04 | 3.099000e+01 | 1.719830e+03 | 1.099920e+07 | 9.990000e+02 | 3.900000e+01 | 3.300000e+01 | 226.000000 | ... | 3.0 | 943.940000 | 3.0 | 37.000000 | 2343.150000 | 39542.450000 | 1407.860000 | 33601.000000 | 521.350000 | 181.000000 |
8 rows × 106 columns
In [6]:
print("Revisión previa de las dimensiones", loan.shape)
Revisión previa de las dimensiones (1306387, 142)
In [7]:
loan.columns
Out[7]:
Index(['loan_amnt', 'funded_amnt', 'funded_amnt_inv', 'term', 'int_rate',
'installment', 'grade', 'sub_grade', 'emp_title', 'emp_length',
...
'hardship_payoff_balance_amount', 'hardship_last_payment_amount',
'disbursement_method', 'debt_settlement_flag',
'debt_settlement_flag_date', 'settlement_status', 'settlement_date',
'settlement_amount', 'settlement_percentage', 'settlement_term'],
dtype='object', length=142)
Variables Categóricas y Fechas¶
In [8]:
for col in loan.select_dtypes(include=['object']).columns:
print ("Column {} has {} unique instances and NaN {}%".format( col, len(loan[col].unique()),round(loan[col].isna().sum()/len(loan)*100,2)) )
Column term has 2 unique instances and NaN 0.0% Column grade has 7 unique instances and NaN 0.0% Column sub_grade has 35 unique instances and NaN 0.0% Column emp_title has 372942 unique instances and NaN 6.33% Column emp_length has 12 unique instances and NaN 5.78% Column home_ownership has 6 unique instances and NaN 0.0% Column verification_status has 3 unique instances and NaN 0.0% Column issue_d has 139 unique instances and NaN 0.0% Column loan_status has 5 unique instances and NaN 0.0% Column pymnt_plan has 1 unique instances and NaN 0.0% Column desc has 124204 unique instances and NaN 90.37% Column purpose has 14 unique instances and NaN 0.0% Column title has 63149 unique instances and NaN 1.18% Column zip_code has 946 unique instances and NaN 0.0% Column addr_state has 51 unique instances and NaN 0.0% Column earliest_cr_line has 739 unique instances and NaN 0.0% Column initial_list_status has 2 unique instances and NaN 0.0% Column last_pymnt_d has 136 unique instances and NaN 0.17% Column next_pymnt_d has 101 unique instances and NaN 99.79% Column last_credit_pull_d has 141 unique instances and NaN 0.0% Column application_type has 2 unique instances and NaN 0.0% Column verification_status_joint has 4 unique instances and NaN 98.23% Column sec_app_earliest_cr_line has 561 unique instances and NaN 98.74% Column hardship_flag has 2 unique instances and NaN 0.0% Column hardship_type has 2 unique instances and NaN 99.59% Column hardship_reason has 10 unique instances and NaN 99.59% Column hardship_status has 4 unique instances and NaN 99.59% Column hardship_start_date has 27 unique instances and NaN 99.59% Column hardship_end_date has 25 unique instances and NaN 99.59% Column payment_plan_start_date has 26 unique instances and NaN 99.59% Column hardship_loan_status has 6 unique instances and NaN 99.59% Column disbursement_method has 2 unique instances and NaN 0.0% Column debt_settlement_flag has 2 unique instances and NaN 0.0% Column debt_settlement_flag_date has 83 unique instances and NaN 97.55% Column settlement_status has 4 unique instances and NaN 97.55% Column settlement_date has 90 unique instances and NaN 97.55%
Variables Numéricas¶
In [9]:
for col in loan.select_dtypes(include=['number']).columns:
print ("Column {}: Mean:{}, Min:{}, Max:{}, NaN: {}%".format( col,round(loan[col].mean(),2),round(loan[col].min(),2), round(loan[col].max(),2),round(loan[col].isna().sum()/len(loan)*100,2)) )
Column loan_amnt: Mean:14405.52, Min:500, Max:40000, NaN: 0.0% Column funded_amnt: Mean:14396.43, Min:500, Max:40000, NaN: 0.0% Column funded_amnt_inv: Mean:14368.1, Min:0.0, Max:40000.0, NaN: 0.0% Column int_rate: Mean:13.26, Min:5.31, Max:30.99, NaN: 0.0% Column installment: Mean:437.78, Min:4.93, Max:1719.83, NaN: 0.0% Column annual_inc: Mean:76148.79, Min:0.0, Max:10999200.0, NaN: 0.0% Column dti: Mean:18.25, Min:-1.0, Max:999.0, NaN: 0.02% Column delinq_2yrs: Mean:0.32, Min:0.0, Max:39.0, NaN: 0.0% Column inq_last_6mths: Mean:0.67, Min:0.0, Max:33.0, NaN: 0.0% Column mths_since_last_delinq: Mean:34.3, Min:0.0, Max:226.0, NaN: 50.45% Column mths_since_last_record: Mean:70.44, Min:0.0, Max:129.0, NaN: 82.96% Column open_acc: Mean:11.59, Min:0.0, Max:90.0, NaN: 0.0% Column pub_rec: Mean:0.22, Min:0.0, Max:86.0, NaN: 0.0% Column revol_bal: Mean:16259.44, Min:0, Max:2904836, NaN: 0.0% Column revol_util: Mean:51.91, Min:0.0, Max:892.3, NaN: 0.07% Column total_acc: Mean:25.01, Min:1.0, Max:176.0, NaN: 0.0% Column out_prncp: Mean:0.27, Min:0.0, Max:37138.1, NaN: 0.0% Column out_prncp_inv: Mean:0.27, Min:0.0, Max:37138.1, NaN: 0.0% Column total_pymnt: Mean:14778.94, Min:0.0, Max:63296.88, NaN: 0.0% Column total_pymnt_inv: Mean:14749.64, Min:0.0, Max:63296.88, NaN: 0.0% Column total_rec_prncp: Mean:12154.8, Min:0.0, Max:40000.0, NaN: 0.0% Column total_rec_int: Mean:2387.11, Min:0.0, Max:28192.5, NaN: 0.0% Column total_rec_late_fee: Mean:1.55, Min:-0.0, Max:1188.83, NaN: 0.0% Column recoveries: Mean:235.47, Min:0.0, Max:39859.55, NaN: 0.0% Column collection_recovery_fee: Mean:39.1, Min:0.0, Max:7174.72, NaN: 0.0% Column last_pymnt_amnt: Mean:5459.7, Min:0.0, Max:42192.05, NaN: 0.0% Column collections_12_mths_ex_med: Mean:0.02, Min:0.0, Max:20.0, NaN: 0.01% Column mths_since_last_major_derog: Mean:43.69, Min:0.0, Max:226.0, NaN: 73.79% Column policy_code: Mean:1.0, Min:1, Max:1, NaN: 0.0% Column annual_inc_joint: Mean:117012.41, Min:11000.0, Max:1350000.0, NaN: 98.22% Column dti_joint: Mean:18.88, Min:0.0, Max:69.49, NaN: 98.22% Column acc_now_delinq: Mean:0.01, Min:0.0, Max:14.0, NaN: 0.0% Column tot_coll_amt: Mean:249.43, Min:0.0, Max:9152545.0, NaN: 5.38% Column tot_cur_bal: Mean:141079.6, Min:0.0, Max:8000078.0, NaN: 5.38% Column open_acc_6m: Mean:1.06, Min:0.0, Max:18.0, NaN: 61.76% Column open_act_il: Mean:2.8, Min:0.0, Max:57.0, NaN: 61.76% Column open_il_12m: Mean:0.78, Min:0.0, Max:25.0, NaN: 61.76% Column open_il_24m: Mean:1.76, Min:0.0, Max:51.0, NaN: 61.76% Column mths_since_rcnt_il: Mean:19.42, Min:0.0, Max:511.0, NaN: 62.75% Column total_bal_il: Mean:35982.15, Min:0.0, Max:1711009.0, NaN: 61.76% Column il_util: Mean:71.54, Min:0.0, Max:558.0, NaN: 66.88% Column open_rv_12m: Mean:1.43, Min:0.0, Max:28.0, NaN: 61.76% Column open_rv_24m: Mean:3.02, Min:0.0, Max:53.0, NaN: 61.76% Column max_bal_bc: Mean:5527.69, Min:0.0, Max:776843.0, NaN: 61.76% Column all_util: Mean:58.23, Min:0.0, Max:198.0, NaN: 61.76% Column total_rev_hi_lim: Mean:32707.97, Min:0.0, Max:9999999.0, NaN: 5.38% Column inq_fi: Mean:1.1, Min:0.0, Max:48.0, NaN: 61.76% Column total_cu_tl: Mean:1.62, Min:0.0, Max:79.0, NaN: 61.76% Column inq_last_12m: Mean:2.33, Min:0.0, Max:67.0, NaN: 61.76% Column acc_open_past_24mths: Mean:4.7, Min:0.0, Max:64.0, NaN: 3.83% Column avg_cur_bal: Mean:13487.68, Min:0.0, Max:958084.0, NaN: 5.38% Column bc_open_to_buy: Mean:10125.2, Min:0.0, Max:559912.0, NaN: 4.85% Column bc_util: Mean:60.07, Min:0.0, Max:339.6, NaN: 4.91% Column chargeoff_within_12_mths: Mean:0.01, Min:0.0, Max:10.0, NaN: 0.01% Column delinq_amnt: Mean:15.03, Min:0.0, Max:249925.0, NaN: 0.0% Column mo_sin_old_il_acct: Mean:125.84, Min:0.0, Max:999.0, NaN: 8.19% Column mo_sin_old_rev_tl_op: Mean:181.5, Min:2.0, Max:852.0, NaN: 5.38% Column mo_sin_rcnt_rev_tl_op: Mean:13.09, Min:0.0, Max:438.0, NaN: 5.38% Column mo_sin_rcnt_tl: Mean:7.84, Min:0.0, Max:314.0, NaN: 5.38% Column mort_acc: Mean:1.68, Min:0.0, Max:51.0, NaN: 3.83% Column mths_since_recent_bc: Mean:23.79, Min:0.0, Max:639.0, NaN: 4.78% Column mths_since_recent_bc_dlq: Mean:39.69, Min:0.0, Max:202.0, NaN: 76.33% Column mths_since_recent_inq: Mean:6.7, Min:0.0, Max:25.0, NaN: 13.19% Column mths_since_recent_revol_delinq: Mean:35.83, Min:0.0, Max:202.0, NaN: 66.64% Column num_accts_ever_120_pd: Mean:0.51, Min:0.0, Max:51.0, NaN: 5.38% Column num_actv_bc_tl: Mean:3.64, Min:0.0, Max:35.0, NaN: 5.38% Column num_actv_rev_tl: Mean:5.65, Min:0.0, Max:63.0, NaN: 5.38% Column num_bc_sats: Mean:4.73, Min:0.0, Max:63.0, NaN: 4.48% Column num_bc_tl: Mean:8.12, Min:0.0, Max:70.0, NaN: 5.38% Column num_il_tl: Mean:8.57, Min:0.0, Max:159.0, NaN: 5.38% Column num_op_rev_tl: Mean:8.28, Min:0.0, Max:83.0, NaN: 5.38% Column num_rev_accts: Mean:14.64, Min:0.0, Max:128.0, NaN: 5.38% Column num_rev_tl_bal_gt_0: Mean:5.6, Min:0.0, Max:45.0, NaN: 5.38% Column num_sats: Mean:11.64, Min:0.0, Max:90.0, NaN: 4.48% Column num_tl_120dpd_2m: Mean:0.0, Min:0.0, Max:6.0, NaN: 9.06% Column num_tl_30dpd: Mean:0.0, Min:0.0, Max:4.0, NaN: 5.38% Column num_tl_90g_dpd_24m: Mean:0.09, Min:0.0, Max:39.0, NaN: 5.38% Column num_tl_op_past_12m: Mean:2.18, Min:0.0, Max:32.0, NaN: 5.38% Column pct_tl_nvr_dlq: Mean:94.17, Min:0.0, Max:100.0, NaN: 5.39% Column percent_bc_gt_75: Mean:45.32, Min:0.0, Max:100.0, NaN: 4.88% Column pub_rec_bankruptcies: Mean:0.13, Min:0.0, Max:12.0, NaN: 0.1% Column tax_liens: Mean:0.05, Min:0.0, Max:85.0, NaN: 0.01% Column tot_hi_cred_lim: Mean:174211.06, Min:0.0, Max:9999999.0, NaN: 5.38% Column total_bal_ex_mort: Mean:49614.18, Min:0.0, Max:3408095.0, NaN: 3.83% Column total_bc_limit: Mean:21566.24, Min:0.0, Max:1105500.0, NaN: 3.83% Column total_il_high_credit_limit: Mean:42067.17, Min:0.0, Max:2101913.0, NaN: 5.38% Column revol_bal_joint: Mean:31352.4, Min:0.0, Max:357135.0, NaN: 98.74% Column sec_app_inq_last_6mths: Mean:0.76, Min:0.0, Max:6.0, NaN: 98.74% Column sec_app_mort_acc: Mean:1.67, Min:0.0, Max:18.0, NaN: 98.74% Column sec_app_open_acc: Mean:11.31, Min:0.0, Max:82.0, NaN: 98.74% Column sec_app_revol_util: Mean:57.35, Min:0.0, Max:212.6, NaN: 98.76% Column sec_app_open_act_il: Mean:2.95, Min:0.0, Max:38.0, NaN: 98.74% Column sec_app_num_rev_accts: Mean:12.82, Min:0.0, Max:92.0, NaN: 98.74% Column sec_app_chargeoff_within_12_mths: Mean:0.06, Min:0.0, Max:20.0, NaN: 98.74% Column sec_app_collections_12_mths_ex_med: Mean:0.09, Min:0.0, Max:16.0, NaN: 98.74% Column sec_app_mths_since_last_major_derog: Mean:36.34, Min:0.0, Max:132.0, NaN: 99.55% Column deferral_term: Mean:3.0, Min:3.0, Max:3.0, NaN: 99.59% Column hardship_amount: Mean:148.21, Min:0.64, Max:943.94, NaN: 99.59% Column hardship_length: Mean:3.0, Min:3.0, Max:3.0, NaN: 99.59% Column hardship_dpd: Mean:13.97, Min:0.0, Max:37.0, NaN: 99.59% Column orig_projected_additional_accrued_interest: Mean:414.08, Min:1.92, Max:2343.15, NaN: 99.74% Column hardship_payoff_balance_amount: Mean:11028.73, Min:55.73, Max:39542.45, NaN: 99.59% Column hardship_last_payment_amount: Mean:184.58, Min:0.01, Max:1407.86, NaN: 99.59% Column settlement_amount: Mean:5051.68, Min:44.21, Max:33601.0, NaN: 97.55% Column settlement_percentage: Mean:47.67, Min:0.2, Max:521.35, NaN: 97.55% Column settlement_term: Mean:13.09, Min:0.0, Max:181.0, NaN: 97.55%
Análisis Exploratorio (EDA)¶
Target Variable¶
In [10]:
plt.figure(figsize = (14,6))
plt.style.use("seaborn-pastel")
g = sns.countplot(x="loan_status", data=loan)
g.set_xticklabels(g.get_xticklabels(),rotation=45)
g.set_xlabel("Loan Status", fontsize=12)
g.set_ylabel("Cantidad", fontsize=15)
g.set_title("Distribucion del Estado de Préstamo", fontsize=20)
sizes=[]
for p in g.patches:
height = p.get_height()
sizes.append(height)
g.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/len(loan)*100),
ha="center", fontsize=12)
g.set_ylim(0, max(sizes) * 1.10)
plt.show()
In [11]:
def calc_risk(data):
status = data['loan_status']
if (status == 'Fully Paid' or status == 'Does not meet the credit policy. Status:Fully Paid'):
return "No Default"
return "Default"
loan['credit_risk'] = loan.apply(calc_risk, axis=1)
In [12]:
plt.figure(figsize = (14,6))
plt.style.use('seaborn-pastel')
g = sns.countplot(x="credit_risk", data=loan)
g.set_xticklabels(g.get_xticklabels(),rotation=45)
g.set_xlabel("Credit Risk", fontsize=12)
g.set_ylabel("Cantidad", fontsize=15)
g.set_title("Distribucion del Riesgo Crediticio", fontsize=20)
sizes=[]
for p in g.patches:
height = p.get_height()
sizes.append(height)
g.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/len(loan)*100),
ha="center", fontsize=12)
g.set_ylim(0, max(sizes) * 1.10)
plt.show()
Variables Independientes¶
Las variables explicativas las agruparemos por tipo de información que entregan para un análisis detallado, es así que se tendrán los siguientes grupos:
- Caracteristicas del Préstamo
- Caracteristicas del Prestatario
- Evaluación del Prestatario
- Historial Crediticio del Prestatario
- Caracteristicas Financieras Actuales
- Rendimiento del Préstamo
- Endeudamiento del prestatario
In [13]:
def plot_var(col_name, full_name, continuous):
fig, (ax1, ax2) = plt.subplots(1, 2, sharex=False, figsize=(15,3))
# Plot 1
plt.style.use('seaborn-pastel')
if continuous:
sns.distplot(loan.loc[loan[col_name].notnull(), col_name], kde=False, ax=ax1)
else:
sns.countplot(loan[col_name], order=sorted(loan[col_name].unique()), color='#5975A4', saturation=1, ax=ax1)
ax1.set_xlabel(full_name)
ax1.set_ylabel('Count')
ax1.set_title(full_name)
# Plot 2
if continuous:
sns.boxplot(x=col_name, y='credit_risk', data=loan, ax=ax2)
ax2.set_ylabel('')
ax2.set_title(full_name + ' por Riesgo Crediticio')
else:
Default_rates = loan.groupby(col_name)['credit_risk'].value_counts(normalize=True)[:,'Default']
sns.barplot(x=Default_rates.index, y=Default_rates.values, color='#5975A4', saturation=1, ax=ax2)
ax2.set_ylabel('Proporción de Préstamos Default')
ax2.set_title('Default por ' + full_name)
ax2.set_xlabel(full_name)
# Plot 3
if continuous:
facet = sns.FacetGrid(loan, hue = 'credit_risk', size=3, aspect=4)
facet.map(sns.kdeplot, col_name, shade=True)
facet.add_legend()
else:
fig = plt.figure(figsize=(12,3))
sns.countplot(x=col_name, hue='credit_risk', data=loan, order=sorted(loan[col_name].unique()) )
plt.tight_layout()
In [14]:
def matrix_corr(df, mirror):
plt.style.use('seaborn-pastel')
corr = df.corr(method='pearson')
fig, ax = plt.subplots(figsize=(10, 10))
colormap = sns.diverging_palette(10,220, as_cmap=True)
if mirror == True:
sns.heatmap(corr, cmap="coolwarm", annot=True, fmt=".2f")
plt.xticks(range(len(corr.columns)), corr.columns);
plt.yticks(range(len(corr.columns)), corr.columns)
else:
dropSelf = np.zeros_like(corr)
dropSelf[np.triu_indices_from(dropSelf)] = True
colormap = sns.diverging_palette(220, 10, as_cmap=True)
sns.heatmap(corr, cmap="coolwarm", annot=True, fmt=".2f", mask=dropSelf)
plt.xticks(range(len(corr.columns)), corr.columns);
plt.yticks(range(len(corr.columns)), corr.columns)
plt.show()
Secondary Applicant¶
Se decide prescindir de todas las observaciones en las que haya un secondary applicant. Tomamos esta decisión con el objetivo de crear un modelo que sea capaz de hacer una predicción lo más precisa posible, y entendemos que la información asimétrica entre los préstamos con y sin secondary applicant podría distorsionar nuestro modelo.
Eliminamos, por tanto, las observaciones con "Joint App" y las columnas que recogen la información referente a este tipo de créditos.
In [15]:
loan.drop(['annual_inc_joint','dti_joint','verification_status_joint',
'revol_bal_joint','sec_app_earliest_cr_line',
'sec_app_inq_last_6mths','sec_app_mort_acc',
'sec_app_open_acc','sec_app_revol_util',
'sec_app_open_act_il','sec_app_num_rev_accts',
'sec_app_chargeoff_within_12_mths',
'sec_app_collections_12_mths_ex_med',
'sec_app_mths_since_last_major_derog'],
axis = 1, inplace = True)
Application Type¶
In [16]:
loan['application_type'].value_counts()
Out[16]:
Individual 1283143 Joint App 23244 Name: application_type, dtype: int64
In [17]:
plot_var('application_type', 'Application Type', continuous=False)
Nuesto modelo sólo considerará préstamos individuales, es así que quitaremos lo préstamos compartidos (Joint App).
In [18]:
loan = loan[loan.application_type == 'Individual']
In [19]:
loan.drop(['application_type'],1, inplace=True)
loan.shape
Out[19]:
(1283143, 128)
Tax Liens¶
Cuando el propietario de un bien, deja de pagar los impuestos correspondientes sobre este, el condado o la ciudad donde se encuentra este bien puede colocar un embargo sobre este bien en propiedad (lien). Por tanto este lien o gravamen es un reclamo legal hacia la propiedad que no ha pagado sus impuestos por el monto adeudado.
In [20]:
loan['tax_liens'].isnull().sum()
Out[20]:
105
In [21]:
loan['tax_liens'].fillna(0, inplace = True)
In [22]:
plot_var('tax_liens', 'Tax liens', continuous=False)
Caracteristicas del Préstamo¶
En este apartado se agruparan las variables que hacen referencia al préstamo. Estas variables son:
- 'Loan Amount',The listed amount of the loan applied for by the borrower. If at some point in time, the credit department reduces the loan amount, then it will be reflected in this value.
- 'Funded Amount', The total amount committed to that loan at that point in time.
- 'Funded Amount Inv',The total amount committed by investors for that loan at that point in time.
- 'Term', The number of payments on the loan. Values are in months and can be either 36 or 60.
- 'Installment',The monthly payment owed by the borrower if the loan originates.
- 'Issue Date',The month which the loan was funded.
- 'Payment Plan',Indicates if a payment plan has been put in place for the loan.
- 'Desc',Loan description provided by the borrower.
- 'Purpose',A category provided by the borrower for the loan request.
- 'Title',The loan title provided by the borrower.
- 'Earliest CR Line',The month the borrower's earliest reported credit line was opened.
- 'Initial List Status', The initial listing status of the loan. Possible values are – W, F.
- 'Policy Code', publicly available policy_code=1, new products not publicly available policy_code=2.
- 'Application Type', Indicates whether the loan is an individual application or a joint application with two co-borrowers.
In [23]:
matrix_corr(loan[['loan_amnt','funded_amnt','funded_amnt_inv','installment']], mirror=False)
Loan Amount¶
El monto listado del préstamo solicitado por el prestatario. Si en algún momento, el departamento de crédito reduce el monto del préstamo, entonces se reflejará en este valor.
In [24]:
plot_var('loan_amnt', 'Loan Amount', continuous=True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
No hay valores ausentes en Loan_Amnt. Se realizará rangos del monto de préstamo dado que se visualiza ciertos valores que tienen frecuencia similar.
In [25]:
round(loan["loan_amnt"].isna().sum()/len(loan)*100,2)
Out[25]:
0.0
In [26]:
purp_loan= ['loan_amnt', 'credit_risk']
cm = sns.light_palette("purple", as_cmap=True)
(round(pd.crosstab(loan[purp_loan[0]], loan[purp_loan[1]],
normalize='columns') * 100,2)).style.background_gradient(cmap = cm)
Out[26]:
| credit_risk | Default | No Default |
|---|---|---|
| loan_amnt | ||
| 500 | 0 | 0 |
| 550 | 0 | 0 |
| 600 | 0 | 0 |
| 700 | 0 | 0 |
| 725 | 0 | 0 |
| 750 | 0 | 0 |
| 800 | 0 | 0 |
| 850 | 0 | 0 |
| 900 | 0 | 0 |
| 925 | 0 | 0 |
| 950 | 0 | 0 |
| 1000 | 0.28 | 0.46 |
| 1025 | 0 | 0 |
| 1050 | 0 | 0 |
| 1075 | 0 | 0 |
| 1100 | 0.01 | 0.01 |
| 1125 | 0 | 0 |
| 1150 | 0 | 0 |
| 1175 | 0 | 0 |
| 1200 | 0.11 | 0.19 |
| 1225 | 0 | 0 |
| 1250 | 0 | 0.01 |
| 1275 | 0 | 0 |
| 1300 | 0.01 | 0.02 |
| 1325 | 0 | 0 |
| 1350 | 0 | 0 |
| 1375 | 0 | 0 |
| 1400 | 0.04 | 0.07 |
| 1425 | 0 | 0 |
| 1450 | 0.01 | 0.02 |
| 1475 | 0 | 0 |
| 1500 | 0.17 | 0.3 |
| 1525 | 0 | 0 |
| 1550 | 0 | 0.01 |
| 1575 | 0 | 0 |
| 1600 | 0.07 | 0.12 |
| 1625 | 0 | 0 |
| 1650 | 0 | 0 |
| 1675 | 0.01 | 0.01 |
| 1700 | 0.01 | 0.02 |
| 1725 | 0 | 0 |
| 1750 | 0.01 | 0.01 |
| 1775 | 0 | 0 |
| 1800 | 0.08 | 0.14 |
| 1825 | 0 | 0 |
| 1850 | 0 | 0 |
| 1875 | 0.01 | 0 |
| 1900 | 0.01 | 0.01 |
| 1925 | 0.01 | 0.01 |
| 1950 | 0.01 | 0.01 |
| 1975 | 0 | 0 |
| 2000 | 0.55 | 0.76 |
| 2025 | 0 | 0 |
| 2050 | 0.01 | 0.01 |
| 2075 | 0 | 0.01 |
| 2100 | 0.05 | 0.07 |
| 2125 | 0.01 | 0.01 |
| 2150 | 0.01 | 0.01 |
| 2175 | 0 | 0 |
| 2200 | 0.07 | 0.08 |
| 2225 | 0.01 | 0 |
| 2250 | 0.01 | 0.02 |
| 2275 | 0.01 | 0.01 |
| 2300 | 0.01 | 0.03 |
| 2325 | 0.01 | 0 |
| 2350 | 0.01 | 0.01 |
| 2375 | 0.01 | 0.01 |
| 2400 | 0.23 | 0.31 |
| 2425 | 0.01 | 0.01 |
| 2450 | 0 | 0.01 |
| 2475 | 0 | 0 |
| 2500 | 0.3 | 0.48 |
| 2525 | 0.01 | 0.01 |
| 2550 | 0.01 | 0.01 |
| 2575 | 0.01 | 0.01 |
| 2600 | 0.03 | 0.04 |
| 2625 | 0.01 | 0.01 |
| 2650 | 0.02 | 0.02 |
| 2675 | 0 | 0.01 |
| 2700 | 0.03 | 0.05 |
| 2725 | 0.01 | 0.01 |
| 2750 | 0.02 | 0.02 |
| 2775 | 0.01 | 0.01 |
| 2800 | 0.12 | 0.16 |
| 2825 | 0.01 | 0.01 |
| 2850 | 0.01 | 0.01 |
| 2875 | 0.02 | 0.02 |
| 2900 | 0.01 | 0.02 |
| 2925 | 0.01 | 0.01 |
| 2950 | 0.01 | 0.01 |
| 2975 | 0.01 | 0.01 |
| 3000 | 1.01 | 1.39 |
| 3025 | 0.04 | 0.05 |
| 3050 | 0.02 | 0.01 |
| 3075 | 0.01 | 0.01 |
| 3100 | 0.01 | 0.02 |
| 3125 | 0.02 | 0.02 |
| 3150 | 0.01 | 0.01 |
| 3175 | 0.01 | 0.01 |
| 3200 | 0.14 | 0.22 |
| 3225 | 0.01 | 0.01 |
| 3250 | 0.05 | 0.04 |
| 3275 | 0.01 | 0.01 |
| 3300 | 0.02 | 0.04 |
| 3325 | 0.01 | 0.01 |
| 3350 | 0.02 | 0.02 |
| 3375 | 0.01 | 0.01 |
| 3400 | 0.02 | 0.03 |
| 3425 | 0.01 | 0.01 |
| 3450 | 0.01 | 0.01 |
| 3475 | 0.01 | 0.01 |
| 3500 | 0.32 | 0.52 |
| 3525 | 0.01 | 0.01 |
| 3550 | 0.02 | 0.01 |
| 3575 | 0.01 | 0.01 |
| 3600 | 0.36 | 0.45 |
| 3625 | 0.02 | 0.03 |
| 3650 | 0.01 | 0.01 |
| 3675 | 0.01 | 0.01 |
| 3700 | 0.02 | 0.03 |
| 3725 | 0.01 | 0.01 |
| 3750 | 0.02 | 0.02 |
| 3775 | 0.01 | 0.01 |
| 3800 | 0.02 | 0.04 |
| 3825 | 0.02 | 0.02 |
| 3850 | 0.02 | 0.02 |
| 3875 | 0.01 | 0.01 |
| 3900 | 0.03 | 0.04 |
| 3925 | 0.01 | 0.01 |
| 3950 | 0.02 | 0.02 |
| 3975 | 0.02 | 0.02 |
| 4000 | 1.09 | 1.5 |
| 4025 | 0.01 | 0.01 |
| 4050 | 0.01 | 0.01 |
| 4075 | 0.02 | 0.01 |
| 4100 | 0.02 | 0.02 |
| 4125 | 0.02 | 0.02 |
| 4150 | 0.02 | 0.01 |
| 4175 | 0.01 | 0.01 |
| 4200 | 0.25 | 0.3 |
| 4225 | 0.02 | 0.01 |
| 4250 | 0.03 | 0.02 |
| 4275 | 0.01 | 0.01 |
| 4300 | 0.03 | 0.04 |
| 4325 | 0.02 | 0.02 |
| 4350 | 0.02 | 0.02 |
| 4375 | 0.03 | 0.03 |
| 4400 | 0.03 | 0.06 |
| 4425 | 0.01 | 0.01 |
| 4450 | 0.02 | 0.03 |
| 4475 | 0.02 | 0.02 |
| 4500 | 0.24 | 0.35 |
| 4525 | 0.02 | 0.01 |
| 4550 | 0.02 | 0.02 |
| 4575 | 0.02 | 0.01 |
| 4600 | 0.03 | 0.04 |
| 4625 | 0.02 | 0.02 |
| 4650 | 0.02 | 0.01 |
| 4675 | 0.02 | 0.02 |
| 4700 | 0.02 | 0.03 |
| 4725 | 0.02 | 0.02 |
| 4750 | 0.06 | 0.05 |
| 4775 | 0.02 | 0.01 |
| 4800 | 0.49 | 0.62 |
| 4825 | 0.02 | 0.01 |
| 4850 | 0.02 | 0.02 |
| 4875 | 0.02 | 0.02 |
| 4900 | 0.03 | 0.05 |
| 4925 | 0.01 | 0.01 |
| 4950 | 0.04 | 0.04 |
| 4975 | 0.01 | 0.01 |
| 5000 | 3.04 | 3.79 |
| 5025 | 0.02 | 0.01 |
| 5050 | 0.02 | 0.03 |
| 5075 | 0.02 | 0.02 |
| 5100 | 0.04 | 0.04 |
| 5125 | 0.03 | 0.02 |
| 5150 | 0.02 | 0.02 |
| 5175 | 0.02 | 0.02 |
| 5200 | 0.07 | 0.11 |
| 5225 | 0.02 | 0.02 |
| 5250 | 0.03 | 0.03 |
| 5275 | 0.02 | 0.02 |
| 5300 | 0.03 | 0.05 |
| 5325 | 0.02 | 0.02 |
| 5350 | 0.01 | 0.01 |
| 5375 | 0.06 | 0.06 |
| 5400 | 0.11 | 0.16 |
| 5425 | 0.01 | 0.01 |
| 5450 | 0.02 | 0.02 |
| 5475 | 0.01 | 0.01 |
| 5500 | 0.26 | 0.42 |
| 5525 | 0.02 | 0.02 |
| 5550 | 0.02 | 0.02 |
| 5575 | 0.02 | 0.01 |
| 5600 | 0.31 | 0.42 |
| 5625 | 0.01 | 0.01 |
| 5650 | 0.02 | 0.02 |
| 5675 | 0.02 | 0.01 |
| 5700 | 0.04 | 0.05 |
| 5725 | 0.02 | 0.02 |
| 5750 | 0.04 | 0.04 |
| 5775 | 0.01 | 0.01 |
| 5800 | 0.04 | 0.05 |
| 5825 | 0.02 | 0.02 |
| 5850 | 0.02 | 0.02 |
| 5875 | 0.03 | 0.03 |
| 5900 | 0.02 | 0.02 |
| 5925 | 0.02 | 0.01 |
| 5950 | 0.04 | 0.03 |
| 5975 | 0.03 | 0.02 |
| 6000 | 2.48 | 3.57 |
| 6025 | 0.14 | 0.12 |
| 6050 | 0.02 | 0.02 |
| 6075 | 0.06 | 0.05 |
| 6100 | 0.04 | 0.03 |
| 6125 | 0.02 | 0.01 |
| 6150 | 0.03 | 0.02 |
| 6175 | 0.01 | 0.01 |
| 6200 | 0.04 | 0.06 |
| 6225 | 0.02 | 0.02 |
| 6250 | 0.07 | 0.07 |
| 6275 | 0.01 | 0.01 |
| 6300 | 0.06 | 0.07 |
| 6325 | 0.03 | 0.02 |
| 6350 | 0.04 | 0.03 |
| 6375 | 0.02 | 0.02 |
| 6400 | 0.32 | 0.39 |
| 6425 | 0.02 | 0.01 |
| 6450 | 0.02 | 0.02 |
| 6475 | 0.03 | 0.02 |
| 6500 | 0.29 | 0.51 |
| 6525 | 0.01 | 0.01 |
| 6550 | 0.01 | 0.01 |
| 6575 | 0.02 | 0.01 |
| 6600 | 0.1 | 0.13 |
| 6625 | 0.04 | 0.04 |
| 6650 | 0.02 | 0.02 |
| 6675 | 0.01 | 0.01 |
| 6700 | 0.04 | 0.05 |
| 6725 | 0.03 | 0.02 |
| 6750 | 0.02 | 0.02 |
| 6775 | 0.02 | 0.01 |
| 6800 | 0.06 | 0.09 |
| 6825 | 0.02 | 0.02 |
| 6850 | 0.03 | 0.03 |
| 6875 | 0.01 | 0.01 |
| 6900 | 0.03 | 0.03 |
| 6925 | 0.02 | 0.02 |
| 6950 | 0.02 | 0.02 |
| 6975 | 0.01 | 0.01 |
| 7000 | 1.35 | 1.89 |
| 7025 | 0.02 | 0.02 |
| 7050 | 0.02 | 0.02 |
| 7075 | 0.02 | 0.02 |
| 7100 | 0.03 | 0.03 |
| 7125 | 0.06 | 0.05 |
| 7150 | 0.03 | 0.02 |
| 7175 | 0.02 | 0.01 |
| 7200 | 0.65 | 0.81 |
| 7225 | 0.03 | 0.02 |
| 7250 | 0.03 | 0.03 |
| 7275 | 0.02 | 0.02 |
| 7300 | 0.03 | 0.04 |
| 7325 | 0.02 | 0.01 |
| 7350 | 0.04 | 0.03 |
| 7375 | 0.03 | 0.02 |
| 7400 | 0.03 | 0.04 |
| 7425 | 0.02 | 0.01 |
| 7450 | 0.03 | 0.02 |
| 7475 | 0.05 | 0.03 |
| 7500 | 0.49 | 0.72 |
| 7525 | 0.02 | 0.01 |
| 7550 | 0.05 | 0.04 |
| 7575 | 0.02 | 0.02 |
| 7600 | 0.03 | 0.05 |
| 7625 | 0.02 | 0.02 |
| 7650 | 0.02 | 0.02 |
| 7675 | 0.03 | 0.02 |
| 7700 | 0.05 | 0.06 |
| 7725 | 0.01 | 0.01 |
| 7750 | 0.06 | 0.05 |
| 7775 | 0.02 | 0.01 |
| 7800 | 0.15 | 0.18 |
| 7825 | 0.02 | 0.01 |
| 7850 | 0.02 | 0.02 |
| 7875 | 0.03 | 0.02 |
| 7900 | 0.05 | 0.04 |
| 7925 | 0.03 | 0.03 |
| 7950 | 0.02 | 0.01 |
| 7975 | 0.03 | 0.02 |
| 8000 | 2.87 | 3.7 |
| 8025 | 0.01 | 0.01 |
| 8050 | 0.03 | 0.02 |
| 8075 | 0.02 | 0.01 |
| 8100 | 0.02 | 0.03 |
| 8125 | 0.03 | 0.03 |
| 8150 | 0.02 | 0.02 |
| 8175 | 0.01 | 0.01 |
| 8200 | 0.04 | 0.05 |
| 8225 | 0.02 | 0.02 |
| 8250 | 0.03 | 0.02 |
| 8275 | 0.03 | 0.02 |
| 8300 | 0.03 | 0.03 |
| 8325 | 0.06 | 0.06 |
| 8350 | 0.02 | 0.02 |
| 8375 | 0.02 | 0.01 |
| 8400 | 0.47 | 0.56 |
| 8425 | 0.02 | 0.01 |
| 8450 | 0.03 | 0.03 |
| 8475 | 0.03 | 0.02 |
| 8500 | 0.28 | 0.42 |
| 8525 | 0.01 | 0.01 |
| 8550 | 0.02 | 0.01 |
| 8575 | 0.03 | 0.03 |
| 8600 | 0.03 | 0.05 |
| 8625 | 0.03 | 0.02 |
| 8650 | 0.02 | 0.02 |
| 8675 | 0.02 | 0.02 |
| 8700 | 0.03 | 0.04 |
| 8725 | 0.03 | 0.03 |
| 8750 | 0.04 | 0.04 |
| 8775 | 0.02 | 0.01 |
| 8800 | 0.09 | 0.12 |
| 8825 | 0.02 | 0.01 |
| 8850 | 0.02 | 0.01 |
| 8875 | 0.09 | 0.08 |
| 8900 | 0.01 | 0.02 |
| 8925 | 0.01 | 0.01 |
| 8950 | 0.04 | 0.03 |
| 8975 | 0.04 | 0.03 |
| 9000 | 0.92 | 1.42 |
| 9025 | 0.03 | 0.01 |
| 9050 | 0.02 | 0.02 |
| 9075 | 0.02 | 0.01 |
| 9100 | 0.08 | 0.07 |
| 9125 | 0.03 | 0.02 |
| 9150 | 0.03 | 0.01 |
| 9175 | 0.04 | 0.03 |
| 9200 | 0.04 | 0.05 |
| 9225 | 0.02 | 0.02 |
| 9250 | 0.05 | 0.05 |
| 9275 | 0.02 | 0.01 |
| 9300 | 0.03 | 0.03 |
| 9325 | 0.04 | 0.03 |
| 9350 | 0.04 | 0.03 |
| 9375 | 0.02 | 0.01 |
| 9400 | 0.03 | 0.03 |
| 9425 | 0.01 | 0.01 |
| 9450 | 0.07 | 0.06 |
| 9475 | 0.02 | 0.01 |
| 9500 | 0.15 | 0.23 |
| 9525 | 0.02 | 0.02 |
| 9550 | 0.03 | 0.02 |
| 9575 | 0.02 | 0.01 |
| 9600 | 0.71 | 0.9 |
| 9625 | 0.02 | 0.01 |
| 9650 | 0.03 | 0.02 |
| 9675 | 0.02 | 0.01 |
| 9700 | 0.04 | 0.03 |
| 9725 | 0.02 | 0.02 |
| 9750 | 0.08 | 0.07 |
| 9775 | 0.02 | 0.01 |
| 9800 | 0.12 | 0.14 |
| 9825 | 0.02 | 0.01 |
| 9850 | 0.03 | 0.02 |
| 9875 | 0.03 | 0.02 |
| 9900 | 0.04 | 0.04 |
| 9925 | 0.02 | 0.03 |
| 9950 | 0.04 | 0.03 |
| 9975 | 0.03 | 0.03 |
| 10000 | 6.88 | 7.53 |
| 10025 | 0.02 | 0.01 |
| 10050 | 0.04 | 0.03 |
| 10075 | 0.07 | 0.04 |
| 10100 | 0.03 | 0.02 |
| 10125 | 0.02 | 0.01 |
| 10150 | 0.02 | 0.02 |
| 10175 | 0.01 | 0.01 |
| 10200 | 0.16 | 0.13 |
| 10225 | 0.02 | 0.01 |
| 10250 | 0.03 | 0.02 |
| 10275 | 0.01 | 0.01 |
| 10300 | 0.03 | 0.03 |
| 10325 | 0.03 | 0.02 |
| 10350 | 0.02 | 0.02 |
| 10375 | 0.03 | 0.03 |
| 10400 | 0.17 | 0.17 |
| 10425 | 0.03 | 0.02 |
| 10450 | 0.02 | 0.02 |
| 10475 | 0.03 | 0.02 |
| 10500 | 0.32 | 0.3 |
| 10525 | 0.03 | 0.02 |
| 10550 | 0.04 | 0.03 |
| 10575 | 0.04 | 0.03 |
| 10600 | 0.03 | 0.03 |
| 10625 | 0.09 | 0.06 |
| 10650 | 0.03 | 0.02 |
| 10675 | 0.02 | 0.01 |
| 10700 | 0.02 | 0.02 |
| 10725 | 0.03 | 0.02 |
| 10750 | 0.07 | 0.05 |
| 10775 | 0.02 | 0.02 |
| 10800 | 0.31 | 0.28 |
| 10825 | 0.02 | 0.01 |
| 10850 | 0.05 | 0.03 |
| 10875 | 0.03 | 0.02 |
| 10900 | 0.02 | 0.02 |
| 10925 | 0.02 | 0.02 |
| 10950 | 0.03 | 0.02 |
| 10975 | 0.05 | 0.02 |
| 11000 | 0.62 | 0.88 |
| 11025 | 0.02 | 0.01 |
| 11050 | 0.03 | 0.02 |
| 11075 | 0.03 | 0.02 |
| 11100 | 0.07 | 0.05 |
| 11125 | 0.02 | 0.01 |
| 11150 | 0.03 | 0.02 |
| 11175 | 0.02 | 0.02 |
| 11200 | 0.66 | 0.45 |
| 11225 | 0.03 | 0.01 |
| 11250 | 0.04 | 0.02 |
| 11275 | 0.01 | 0.01 |
| 11300 | 0.03 | 0.03 |
| 11325 | 0.05 | 0.03 |
| 11350 | 0.02 | 0.01 |
| 11375 | 0.02 | 0.01 |
| 11400 | 0.09 | 0.07 |
| 11425 | 0.02 | 0.01 |
| 11450 | 0.02 | 0.01 |
| 11475 | 0.02 | 0.01 |
| 11500 | 0.22 | 0.24 |
| 11525 | 0.03 | 0.02 |
| 11550 | 0.03 | 0.02 |
| 11575 | 0.02 | 0.01 |
| 11600 | 0.04 | 0.03 |
| 11625 | 0.04 | 0.03 |
| 11650 | 0.02 | 0.02 |
| 11675 | 0.02 | 0.02 |
| 11700 | 0.07 | 0.05 |
| 11725 | 0.02 | 0.01 |
| 11750 | 0.04 | 0.02 |
| 11775 | 0.02 | 0.01 |
| 11800 | 0.02 | 0.02 |
| 11825 | 0.01 | 0.01 |
| 11850 | 0.05 | 0.03 |
| 11875 | 0.06 | 0.03 |
| 11900 | 0.05 | 0.03 |
| 11925 | 0.02 | 0.01 |
| 11950 | 0.04 | 0.03 |
| 11975 | 0.05 | 0.03 |
| 12000 | 5.04 | 5.53 |
| 12025 | 0.01 | 0.01 |
| 12050 | 0.03 | 0.02 |
| 12075 | 0.04 | 0.02 |
| 12100 | 0.02 | 0.02 |
| 12125 | 0.02 | 0.01 |
| 12150 | 0.04 | 0.02 |
| 12175 | 0.05 | 0.04 |
| 12200 | 0.02 | 0.02 |
| 12225 | 0.04 | 0.01 |
| 12250 | 0.07 | 0.05 |
| 12275 | 0.02 | 0.01 |
| 12300 | 0.05 | 0.04 |
| 12325 | 0.02 | 0.01 |
| 12350 | 0.02 | 0.01 |
| 12375 | 0.05 | 0.05 |
| 12400 | 0.03 | 0.03 |
| 12425 | 0.02 | 0.01 |
| 12450 | 0.03 | 0.02 |
| 12475 | 0.02 | 0.01 |
| 12500 | 0.21 | 0.26 |
| 12525 | 0.02 | 0.01 |
| 12550 | 0.03 | 0.01 |
| 12575 | 0.03 | 0.02 |
| 12600 | 0.19 | 0.13 |
| 12625 | 0.02 | 0.01 |
| 12650 | 0.02 | 0.01 |
| 12675 | 0.02 | 0.01 |
| 12700 | 0.03 | 0.03 |
| 12725 | 0.03 | 0.02 |
| 12750 | 0.03 | 0.02 |
| 12775 | 0.01 | 0.01 |
| 12800 | 0.3 | 0.25 |
| 12825 | 0.02 | 0.01 |
| 12850 | 0.02 | 0.01 |
| 12875 | 0.03 | 0.02 |
| 12900 | 0.02 | 0.02 |
| 12925 | 0.02 | 0.01 |
| 12950 | 0.05 | 0.03 |
| 12975 | 0.02 | 0.01 |
| 13000 | 0.62 | 0.88 |
| 13025 | 0.02 | 0.01 |
| 13050 | 0.02 | 0.01 |
| 13075 | 0.01 | 0.01 |
| 13100 | 0.03 | 0.02 |
| 13125 | 0.01 | 0.01 |
| 13150 | 0.04 | 0.02 |
| 13175 | 0.02 | 0.01 |
| 13200 | 0.2 | 0.18 |
| 13225 | 0.03 | 0.02 |
| 13250 | 0.07 | 0.04 |
| 13275 | 0.02 | 0.01 |
| 13300 | 0.04 | 0.03 |
| 13325 | 0.02 | 0.01 |
| 13350 | 0.02 | 0.01 |
| 13375 | 0.03 | 0.02 |
| 13400 | 0.02 | 0.02 |
| 13425 | 0.03 | 0.01 |
| 13450 | 0.02 | 0.01 |
| 13475 | 0.06 | 0.03 |
| 13500 | 0.14 | 0.18 |
| 13525 | 0.01 | 0.01 |
| 13550 | 0.02 | 0.01 |
| 13575 | 0.02 | 0.01 |
| 13600 | 0.12 | 0.09 |
| 13625 | 0.03 | 0.02 |
| 13650 | 0.02 | 0.02 |
| 13675 | 0.03 | 0.02 |
| 13700 | 0.03 | 0.03 |
| 13725 | 0.02 | 0.01 |
| 13750 | 0.05 | 0.03 |
| 13775 | 0.01 | 0.01 |
| 13800 | 0.06 | 0.06 |
| 13825 | 0.02 | 0.01 |
| 13850 | 0.03 | 0.01 |
| 13875 | 0.01 | 0.01 |
| 13900 | 0.02 | 0.02 |
| 13925 | 0.03 | 0.02 |
| 13950 | 0.02 | 0.01 |
| 13975 | 0.02 | 0.01 |
| 14000 | 1.44 | 1.52 |
| 14025 | 0.03 | 0.02 |
| 14050 | 0.03 | 0.01 |
| 14075 | 0.06 | 0.03 |
| 14100 | 0.03 | 0.02 |
| 14125 | 0.04 | 0.04 |
| 14150 | 0.02 | 0.01 |
| 14175 | 0.02 | 0.01 |
| 14200 | 0.02 | 0.02 |
| 14225 | 0.02 | 0.01 |
| 14250 | 0.03 | 0.02 |
| 14275 | 0.02 | 0.01 |
| 14300 | 0.04 | 0.04 |
| 14325 | 0.02 | 0.01 |
| 14350 | 0.04 | 0.02 |
| 14375 | 0.05 | 0.03 |
| 14400 | 0.66 | 0.61 |
| 14425 | 0.02 | 0.01 |
| 14450 | 0.02 | 0.01 |
| 14475 | 0.02 | 0.01 |
| 14500 | 0.1 | 0.12 |
| 14525 | 0.01 | 0.01 |
| 14550 | 0.02 | 0.01 |
| 14575 | 0.03 | 0.02 |
| 14600 | 0.04 | 0.03 |
| 14625 | 0.01 | 0.01 |
| 14650 | 0.01 | 0.01 |
| 14675 | 0.05 | 0.03 |
| 14700 | 0.09 | 0.05 |
| 14725 | 0.02 | 0.01 |
| 14750 | 0.03 | 0.02 |
| 14775 | 0.02 | 0.01 |
| 14800 | 0.04 | 0.03 |
| 14825 | 0.05 | 0.03 |
| 14850 | 0.02 | 0.01 |
| 14875 | 0.02 | 0.01 |
| 14900 | 0.04 | 0.02 |
| 14925 | 0.02 | 0.01 |
| 14950 | 0.03 | 0.02 |
| 14975 | 0.06 | 0.03 |
| 15000 | 5.06 | 5.24 |
| 15025 | 0.01 | 0.01 |
| 15050 | 0.02 | 0.02 |
| 15075 | 0.02 | 0.01 |
| 15100 | 0.04 | 0.02 |
| 15125 | 0.04 | 0.02 |
| 15150 | 0.01 | 0.01 |
| 15175 | 0.02 | 0.01 |
| 15200 | 0.07 | 0.05 |
| 15225 | 0.02 | 0.01 |
| 15250 | 0.06 | 0.04 |
| 15275 | 0.02 | 0.01 |
| 15300 | 0.03 | 0.02 |
| 15325 | 0.02 | 0.01 |
| 15350 | 0.04 | 0.03 |
| 15375 | 0.01 | 0.01 |
| 15400 | 0.08 | 0.06 |
| 15425 | 0.02 | 0.01 |
| 15450 | 0.03 | 0.02 |
| 15475 | 0.02 | 0.01 |
| 15500 | 0.09 | 0.1 |
| 15525 | 0.01 | 0.01 |
| 15550 | 0.03 | 0.02 |
| 15575 | 0.01 | 0.01 |
| 15600 | 0.16 | 0.16 |
| 15625 | 0.03 | 0.02 |
| 15650 | 0.01 | 0.01 |
| 15675 | 0.01 | 0.01 |
| 15700 | 0.02 | 0.01 |
| 15725 | 0.01 | 0.01 |
| 15750 | 0.03 | 0.02 |
| 15775 | 0.02 | 0.01 |
| 15800 | 0.03 | 0.02 |
| 15825 | 0.04 | 0.02 |
| 15850 | 0.06 | 0.04 |
| 15875 | 0.05 | 0.02 |
| 15900 | 0.02 | 0.02 |
| 15925 | 0.02 | 0.01 |
| 15950 | 0.05 | 0.02 |
| 15975 | 0.02 | 0.01 |
| 16000 | 2.9 | 2.65 |
| 16025 | 0.01 | 0.01 |
| 16050 | 0.01 | 0.01 |
| 16075 | 0.01 | 0.01 |
| 16100 | 0.04 | 0.03 |
| 16125 | 0.01 | 0.01 |
| 16150 | 0.04 | 0.02 |
| 16175 | 0.01 | 0.01 |
| 16200 | 0.09 | 0.05 |
| 16225 | 0.02 | 0.01 |
| 16250 | 0.01 | 0.01 |
| 16275 | 0.02 | 0.01 |
| 16300 | 0.04 | 0.02 |
| 16325 | 0.02 | 0.01 |
| 16350 | 0.01 | 0.01 |
| 16375 | 0.03 | 0.01 |
| 16400 | 0.02 | 0.01 |
| 16425 | 0.04 | 0.03 |
| 16450 | 0.05 | 0.03 |
| 16475 | 0.02 | 0.01 |
| 16500 | 0.11 | 0.13 |
| 16525 | 0.02 | 0.01 |
| 16550 | 0.04 | 0.02 |
| 16575 | 0.02 | 0.01 |
| 16600 | 0.02 | 0.01 |
| 16625 | 0.02 | 0.01 |
| 16650 | 0.02 | 0.01 |
| 16675 | 0.02 | 0.01 |
| 16700 | 0.05 | 0.02 |
| 16725 | 0.01 | 0.01 |
| 16750 | 0.07 | 0.04 |
| 16775 | 0.02 | 0.01 |
| 16800 | 0.34 | 0.28 |
| 16825 | 0.02 | 0.01 |
| 16850 | 0.01 | 0.01 |
| 16875 | 0.01 | 0.01 |
| 16900 | 0.03 | 0.02 |
| 16925 | 0.01 | 0.01 |
| 16950 | 0.05 | 0.03 |
| 16975 | 0.02 | 0.01 |
| 17000 | 0.53 | 0.64 |
| 17025 | 0.01 | 0.01 |
| 17050 | 0.05 | 0.03 |
| 17075 | 0.01 | 0.01 |
| 17100 | 0.05 | 0.02 |
| 17125 | 0.03 | 0.01 |
| 17150 | 0.02 | 0.01 |
| 17175 | 0.01 | 0.01 |
| 17200 | 0.04 | 0.02 |
| 17225 | 0.02 | 0.01 |
| 17250 | 0.02 | 0.01 |
| 17275 | 0.04 | 0.02 |
| 17300 | 0.01 | 0.01 |
| 17325 | 0.03 | 0.02 |
| 17350 | 0.03 | 0.01 |
| 17375 | 0.01 | 0.01 |
| 17400 | 0.03 | 0.02 |
| 17425 | 0.02 | 0.01 |
| 17450 | 0.02 | 0.01 |
| 17475 | 0.05 | 0.03 |
| 17500 | 0.2 | 0.19 |
| 17525 | 0.01 | 0.01 |
| 17550 | 0.03 | 0.01 |
| 17575 | 0.01 | 0.01 |
| 17600 | 0.15 | 0.12 |
| 17625 | 0.07 | 0.04 |
| 17650 | 0.01 | 0.01 |
| 17675 | 0.02 | 0.01 |
| 17700 | 0.02 | 0.01 |
| 17725 | 0.01 | 0 |
| 17750 | 0.02 | 0.01 |
| 17775 | 0.02 | 0.01 |
| 17800 | 0.02 | 0.01 |
| 17825 | 0.01 | 0.01 |
| 17850 | 0.02 | 0.01 |
| 17875 | 0.03 | 0.02 |
| 17900 | 0.01 | 0.01 |
| 17925 | 0.02 | 0.01 |
| 17950 | 0.02 | 0.01 |
| 17975 | 0.04 | 0.02 |
| 18000 | 2.36 | 2.25 |
| 18025 | 0.02 | 0.01 |
| 18050 | 0.02 | 0.01 |
| 18075 | 0.03 | 0.02 |
| 18100 | 0.01 | 0.01 |
| 18125 | 0.02 | 0.01 |
| 18150 | 0.03 | 0.02 |
| 18175 | 0.01 | 0.01 |
| 18200 | 0.06 | 0.05 |
| 18225 | 0.07 | 0.04 |
| 18250 | 0.05 | 0.03 |
| 18275 | 0.02 | 0.01 |
| 18300 | 0.02 | 0.02 |
| 18325 | 0.01 | 0.01 |
| 18350 | 0.04 | 0.02 |
| 18375 | 0.02 | 0.01 |
| 18400 | 0.07 | 0.05 |
| 18425 | 0.02 | 0.01 |
| 18450 | 0.04 | 0.02 |
| 18475 | 0.04 | 0.01 |
| 18500 | 0.08 | 0.09 |
| 18525 | 0.01 | 0.01 |
| 18550 | 0.04 | 0.02 |
| 18575 | 0.01 | 0.01 |
| 18600 | 0.03 | 0.03 |
| 18625 | 0.02 | 0.01 |
| 18650 | 0.01 | 0.01 |
| 18675 | 0.02 | 0.01 |
| 18700 | 0.03 | 0.02 |
| 18725 | 0.03 | 0.01 |
| 18750 | 0.02 | 0.01 |
| 18775 | 0.02 | 0.01 |
| 18800 | 0.02 | 0.02 |
| 18825 | 0.06 | 0.04 |
| 18850 | 0.01 | 0.01 |
| 18875 | 0.01 | 0.01 |
| 18900 | 0.08 | 0.04 |
| 18925 | 0.01 | 0 |
| 18950 | 0.03 | 0.01 |
| 18975 | 0.01 | 0.01 |
| 19000 | 0.27 | 0.3 |
| 19025 | 0.01 | 0.01 |
| 19050 | 0.02 | 0.01 |
| 19075 | 0.04 | 0.03 |
| 19100 | 0.02 | 0.01 |
| 19125 | 0.04 | 0.02 |
| 19150 | 0.03 | 0.01 |
| 19175 | 0.01 | 0.01 |
| 19200 | 0.43 | 0.35 |
| 19225 | 0.02 | 0.01 |
| 19250 | 0.02 | 0.01 |
| 19275 | 0.01 | 0.01 |
| 19300 | 0.04 | 0.02 |
| 19325 | 0.01 | 0.01 |
| 19350 | 0.04 | 0.02 |
| 19375 | 0.02 | 0.01 |
| 19400 | 0.02 | 0.02 |
| 19425 | 0.04 | 0.02 |
| 19450 | 0.01 | 0.01 |
| 19475 | 0.02 | 0.01 |
| 19500 | 0.07 | 0.08 |
| 19525 | 0.01 | 0 |
| 19550 | 0.01 | 0.01 |
| 19575 | 0.02 | 0.01 |
| 19600 | 0.08 | 0.06 |
| 19625 | 0.01 | 0.01 |
| 19650 | 0.02 | 0.01 |
| 19675 | 0.02 | 0.01 |
| 19700 | 0.02 | 0.01 |
| 19725 | 0.02 | 0.01 |
| 19750 | 0.1 | 0.05 |
| 19775 | 0.02 | 0.01 |
| 19800 | 0.11 | 0.06 |
| 19825 | 0.01 | 0.01 |
| 19850 | 0.01 | 0.01 |
| 19875 | 0.02 | 0.01 |
| 19900 | 0.01 | 0.01 |
| 19925 | 0.01 | 0.01 |
| 19950 | 0.04 | 0.02 |
| 19975 | 0.01 | 0.01 |
| 20000 | 5.43 | 5.12 |
| 20025 | 0.01 | 0 |
| 20050 | 0.04 | 0.03 |
| 20075 | 0.01 | 0 |
| 20100 | 0.01 | 0.01 |
| 20125 | 0.05 | 0.03 |
| 20150 | 0.04 | 0.02 |
| 20175 | 0.01 | 0.01 |
| 20200 | 0.02 | 0.01 |
| 20225 | 0.02 | 0.02 |
| 20250 | 0.02 | 0.01 |
| 20275 | 0.02 | 0.01 |
| 20300 | 0.01 | 0.01 |
| 20325 | 0.01 | 0.01 |
| 20350 | 0.01 | 0.01 |
| 20375 | 0.02 | 0.01 |
| 20400 | 0.12 | 0.1 |
| 20425 | 0.01 | 0.01 |
| 20450 | 0.01 | 0.01 |
| 20475 | 0.02 | 0.01 |
| 20500 | 0.07 | 0.05 |
| 20525 | 0.01 | 0.01 |
| 20550 | 0.01 | 0.01 |
| 20575 | 0.02 | 0.01 |
| 20600 | 0.01 | 0.01 |
| 20625 | 0.02 | 0.01 |
| 20650 | 0.01 | 0.01 |
| 20675 | 0.04 | 0.03 |
| 20700 | 0.04 | 0.02 |
| 20725 | 0.01 | 0.01 |
| 20750 | 0.02 | 0.01 |
| 20775 | 0.01 | 0.01 |
| 20800 | 0.08 | 0.06 |
| 20825 | 0.01 | 0.01 |
| 20850 | 0.01 | 0.01 |
| 20875 | 0.02 | 0.01 |
| 20900 | 0.02 | 0.01 |
| 20925 | 0.01 | 0.01 |
| 20950 | 0.05 | 0.03 |
| 20975 | 0.02 | 0.01 |
| 21000 | 1.27 | 1.22 |
| 21025 | 0.01 | 0 |
| 21050 | 0.01 | 0.01 |
| 21075 | 0.03 | 0.01 |
| 21100 | 0.03 | 0.02 |
| 21125 | 0.03 | 0.01 |
| 21150 | 0.01 | 0.01 |
| 21175 | 0.02 | 0.01 |
| 21200 | 0.06 | 0.04 |
| 21225 | 0.01 | 0 |
| 21250 | 0.04 | 0.03 |
| 21275 | 0.02 | 0.01 |
| 21300 | 0.01 | 0.01 |
| 21325 | 0.01 | 0.01 |
| 21350 | 0.03 | 0.01 |
| 21375 | 0.01 | 0 |
| 21400 | 0.02 | 0.01 |
| 21425 | 0.01 | 0.01 |
| 21450 | 0.01 | 0.01 |
| 21475 | 0.02 | 0.01 |
| 21500 | 0.04 | 0.03 |
| 21525 | 0.01 | 0.01 |
| 21550 | 0.02 | 0.01 |
| 21575 | 0.01 | 0 |
| 21600 | 0.22 | 0.18 |
| 21625 | 0.01 | 0.01 |
| 21650 | 0.01 | 0.01 |
| 21675 | 0.01 | 0 |
| 21700 | 0.01 | 0.01 |
| 21725 | 0.04 | 0.02 |
| 21750 | 0.02 | 0.01 |
| 21775 | 0.01 | 0 |
| 21800 | 0.02 | 0.01 |
| 21825 | 0.02 | 0.01 |
| 21850 | 0.04 | 0.03 |
| 21875 | 0.01 | 0.01 |
| 21900 | 0.01 | 0.01 |
| 21925 | 0.01 | 0.01 |
| 21950 | 0.01 | 0.01 |
| 21975 | 0.02 | 0.01 |
| 22000 | 0.51 | 0.54 |
| 22025 | 0.01 | 0.01 |
| 22050 | 0.01 | 0.01 |
| 22075 | 0.02 | 0.01 |
| 22100 | 0.01 | 0.01 |
| 22125 | 0.01 | 0.01 |
| 22150 | 0.01 | 0 |
| 22175 | 0.01 | 0 |
| 22200 | 0.02 | 0.02 |
| 22225 | 0.01 | 0.01 |
| 22250 | 0.05 | 0.03 |
| 22275 | 0 | 0 |
| 22300 | 0.01 | 0.01 |
| 22325 | 0.01 | 0.01 |
| 22350 | 0.01 | 0.01 |
| 22375 | 0.02 | 0.01 |
| 22400 | 0.13 | 0.12 |
| 22425 | 0.02 | 0.01 |
| 22450 | 0.01 | 0.01 |
| 22475 | 0.01 | 0.01 |
| 22500 | 0.1 | 0.08 |
| 22525 | 0.01 | 0.01 |
| 22550 | 0.01 | 0.01 |
| 22575 | 0.01 | 0.01 |
| 22600 | 0.01 | 0.01 |
| 22625 | 0 | 0 |
| 22650 | 0.01 | 0 |
| 22675 | 0.02 | 0.01 |
| 22700 | 0.01 | 0 |
| 22725 | 0.01 | 0 |
| 22750 | 0.06 | 0.04 |
| 22775 | 0.01 | 0 |
| 22800 | 0.1 | 0.06 |
| 22825 | 0.01 | 0 |
| 22850 | 0.02 | 0.01 |
| 22875 | 0.03 | 0.02 |
| 22900 | 0.01 | 0.01 |
| 22925 | 0.01 | 0 |
| 22950 | 0.03 | 0.02 |
| 22975 | 0 | 0 |
| 23000 | 0.22 | 0.28 |
| 23025 | 0.01 | 0.01 |
| 23050 | 0.02 | 0.01 |
| 23075 | 0.01 | 0.01 |
| 23100 | 0.03 | 0.01 |
| 23125 | 0.01 | 0.01 |
| 23150 | 0.01 | 0 |
| 23175 | 0.01 | 0.01 |
| 23200 | 0.02 | 0.02 |
| 23225 | 0.01 | 0.01 |
| 23250 | 0.01 | 0.01 |
| 23275 | 0.01 | 0.01 |
| 23300 | 0.01 | 0.01 |
| 23325 | 0.04 | 0.02 |
| 23350 | 0.01 | 0.01 |
| 23375 | 0.01 | 0 |
| 23400 | 0.03 | 0.02 |
| 23425 | 0.01 | 0.01 |
| 23450 | 0.02 | 0.01 |
| 23475 | 0.01 | 0.01 |
| 23500 | 0.07 | 0.05 |
| 23525 | 0.01 | 0.01 |
| 23550 | 0.02 | 0.01 |
| 23575 | 0.01 | 0 |
| 23600 | 0.02 | 0.01 |
| 23625 | 0.01 | 0 |
| 23650 | 0.01 | 0 |
| 23675 | 0.03 | 0.02 |
| 23700 | 0.01 | 0.01 |
| 23725 | 0.02 | 0.01 |
| 23750 | 0.02 | 0.01 |
| 23775 | 0.01 | 0 |
| 23800 | 0.04 | 0.03 |
| 23825 | 0 | 0 |
| 23850 | 0.05 | 0.03 |
| 23875 | 0.02 | 0.01 |
| 23900 | 0.03 | 0.01 |
| 23925 | 0.03 | 0.01 |
| 23950 | 0.01 | 0 |
| 23975 | 0.03 | 0.01 |
| 24000 | 2.54 | 2.36 |
| 24025 | 0.01 | 0 |
| 24050 | 0.01 | 0 |
| 24075 | 0.01 | 0.01 |
| 24100 | 0.01 | 0 |
| 24125 | 0.03 | 0.01 |
| 24150 | 0.01 | 0.01 |
| 24175 | 0.02 | 0.01 |
| 24200 | 0.03 | 0.01 |
| 24225 | 0.01 | 0 |
| 24250 | 0.05 | 0.04 |
| 24275 | 0.01 | 0 |
| 24300 | 0.01 | 0.01 |
| 24325 | 0 | 0 |
| 24350 | 0.02 | 0.01 |
| 24375 | 0.04 | 0.03 |
| 24400 | 0.01 | 0.01 |
| 24425 | 0.01 | 0 |
| 24450 | 0.02 | 0.01 |
| 24475 | 0.01 | 0.01 |
| 24500 | 0.04 | 0.05 |
| 24525 | 0.02 | 0.01 |
| 24550 | 0.01 | 0.01 |
| 24575 | 0.03 | 0.02 |
| 24600 | 0.01 | 0.01 |
| 24625 | 0.02 | 0.01 |
| 24650 | 0.03 | 0.01 |
| 24675 | 0.01 | 0 |
| 24700 | 0.03 | 0.01 |
| 24725 | 0.01 | 0.01 |
| 24750 | 0.01 | 0.01 |
| 24775 | 0.01 | 0 |
| 24800 | 0.03 | 0.03 |
| 24825 | 0.01 | 0.01 |
| 24850 | 0.01 | 0.01 |
| 24875 | 0.01 | 0 |
| 24900 | 0.01 | 0.01 |
| 24925 | 0.04 | 0.03 |
| 24950 | 0.02 | 0.01 |
| 24975 | 0.01 | 0.01 |
| 25000 | 2.56 | 2.49 |
| 25025 | 0.01 | 0 |
| 25050 | 0.01 | 0.01 |
| 25075 | 0.02 | 0.01 |
| 25100 | 0 | 0 |
| 25125 | 0 | 0 |
| 25150 | 0.01 | 0.01 |
| 25175 | 0.01 | 0 |
| 25200 | 0.11 | 0.07 |
| 25225 | 0 | 0 |
| 25250 | 0.01 | 0.01 |
| 25275 | 0 | 0 |
| 25300 | 0.02 | 0.01 |
| 25325 | 0.01 | 0.01 |
| 25350 | 0.01 | 0 |
| 25375 | 0.01 | 0.01 |
| 25400 | 0 | 0 |
| 25425 | 0.01 | 0 |
| 25450 | 0.03 | 0.02 |
| 25475 | 0.02 | 0.02 |
| 25500 | 0.02 | 0.02 |
| 25525 | 0.01 | 0.01 |
| 25550 | 0.01 | 0 |
| 25575 | 0 | 0 |
| 25600 | 0.06 | 0.06 |
| 25625 | 0.01 | 0 |
| 25650 | 0 | 0 |
| 25675 | 0.01 | 0 |
| 25700 | 0.01 | 0.01 |
| 25725 | 0.01 | 0.01 |
| 25750 | 0.01 | 0.01 |
| 25775 | 0 | 0 |
| 25800 | 0.01 | 0.01 |
| 25825 | 0.01 | 0 |
| 25850 | 0.01 | 0.01 |
| 25875 | 0.01 | 0.01 |
| 25900 | 0.01 | 0.01 |
| 25925 | 0.01 | 0.01 |
| 25950 | 0.01 | 0 |
| 25975 | 0.03 | 0.02 |
| 26000 | 0.17 | 0.19 |
| 26025 | 0.01 | 0 |
| 26050 | 0.01 | 0.01 |
| 26075 | 0 | 0 |
| 26100 | 0.01 | 0 |
| 26125 | 0.01 | 0 |
| 26150 | 0.01 | 0 |
| 26175 | 0 | 0 |
| 26200 | 0.01 | 0.01 |
| 26225 | 0.01 | 0 |
| 26250 | 0.01 | 0.01 |
| 26275 | 0.01 | 0 |
| 26300 | 0.01 | 0.01 |
| 26325 | 0.01 | 0 |
| 26350 | 0.01 | 0 |
| 26375 | 0.05 | 0.03 |
| 26400 | 0.1 | 0.07 |
| 26425 | 0.01 | 0 |
| 26450 | 0.01 | 0 |
| 26475 | 0 | 0 |
| 26500 | 0.08 | 0.05 |
| 26525 | 0.01 | 0 |
| 26550 | 0.01 | 0 |
| 26575 | 0.01 | 0.01 |
| 26600 | 0.02 | 0.01 |
| 26625 | 0.01 | 0 |
| 26650 | 0.01 | 0.01 |
| 26675 | 0.01 | 0.01 |
| 26700 | 0.01 | 0 |
| 26725 | 0 | 0 |
| 26750 | 0.01 | 0.01 |
| 26775 | 0.01 | 0 |
| 26800 | 0.01 | 0.01 |
| 26825 | 0 | 0 |
| 26850 | 0.02 | 0.01 |
| 26875 | 0.01 | 0.01 |
| 26900 | 0 | 0 |
| 26925 | 0.01 | 0.01 |
| 26950 | 0 | 0 |
| 26975 | 0.01 | 0 |
| 27000 | 0.17 | 0.19 |
| 27025 | 0 | 0 |
| 27050 | 0.03 | 0.01 |
| 27075 | 0 | 0 |
| 27100 | 0.01 | 0 |
| 27125 | 0.01 | 0 |
| 27150 | 0.01 | 0.01 |
| 27175 | 0.01 | 0 |
| 27200 | 0.05 | 0.04 |
| 27225 | 0.01 | 0 |
| 27250 | 0 | 0.01 |
| 27275 | 0.01 | 0.01 |
| 27300 | 0.04 | 0.02 |
| 27325 | 0.01 | 0.01 |
| 27350 | 0.01 | 0 |
| 27375 | 0.01 | 0 |
| 27400 | 0.01 | 0 |
| 27425 | 0.01 | 0 |
| 27450 | 0.02 | 0.01 |
| 27475 | 0 | 0 |
| 27500 | 0.05 | 0.05 |
| 27525 | 0.01 | 0.01 |
| 27550 | 0.01 | 0 |
| 27575 | 0.04 | 0.03 |
| 27600 | 0.07 | 0.04 |
| 27625 | 0.01 | 0 |
| 27650 | 0.01 | 0.01 |
| 27675 | 0 | 0 |
| 27700 | 0.01 | 0 |
| 27725 | 0.01 | 0.01 |
| 27750 | 0.01 | 0 |
| 27775 | 0 | 0 |
| 27800 | 0.01 | 0 |
| 27825 | 0 | 0 |
| 27850 | 0.01 | 0 |
| 27875 | 0 | 0 |
| 27900 | 0.01 | 0 |
| 27925 | 0 | 0 |
| 27950 | 0 | 0 |
| 27975 | 0.01 | 0.01 |
| 28000 | 1.31 | 1.48 |
| 28025 | 0 | 0 |
| 28050 | 0.01 | 0 |
| 28075 | 0 | 0 |
| 28100 | 0.03 | 0.02 |
| 28125 | 0.01 | 0 |
| 28150 | 0.01 | 0 |
| 28175 | 0 | 0 |
| 28200 | 0.02 | 0.01 |
| 28225 | 0 | 0 |
| 28250 | 0.01 | 0.01 |
| 28275 | 0 | 0 |
| 28300 | 0 | 0 |
| 28325 | 0 | 0 |
| 28350 | 0.01 | 0 |
| 28375 | 0.01 | 0 |
| 28400 | 0 | 0 |
| 28425 | 0.01 | 0 |
| 28450 | 0 | 0 |
| 28475 | 0 | 0 |
| 28500 | 0.06 | 0.04 |
| 28525 | 0 | 0 |
| 28550 | 0.01 | 0.01 |
| 28575 | 0 | 0 |
| 28600 | 0.02 | 0.01 |
| 28625 | 0.02 | 0.02 |
| 28650 | 0.01 | 0 |
| 28675 | 0 | 0 |
| 28700 | 0.01 | 0 |
| 28725 | 0 | 0 |
| 28750 | 0.01 | 0 |
| 28775 | 0.02 | 0.01 |
| 28800 | 0.09 | 0.06 |
| 28825 | 0 | 0 |
| 28850 | 0 | 0 |
| 28875 | 0.01 | 0 |
| 28900 | 0.01 | 0 |
| 28925 | 0 | 0 |
| 28950 | 0 | 0 |
| 28975 | 0.02 | 0.01 |
| 29000 | 0.07 | 0.08 |
| 29025 | 0.01 | 0 |
| 29050 | 0.01 | 0 |
| 29075 | 0 | 0 |
| 29100 | 0.03 | 0.02 |
| 29125 | 0.01 | 0 |
| 29150 | 0 | 0 |
| 29175 | 0.03 | 0.02 |
| 29200 | 0 | 0 |
| 29225 | 0 | 0 |
| 29250 | 0.02 | 0.01 |
| 29275 | 0.01 | 0 |
| 29300 | 0 | 0 |
| 29325 | 0.02 | 0.01 |
| 29350 | 0.01 | 0 |
| 29375 | 0 | 0 |
| 29400 | 0.02 | 0.01 |
| 29425 | 0.01 | 0 |
| 29450 | 0.01 | 0.01 |
| 29475 | 0.01 | 0 |
| 29500 | 0.02 | 0.02 |
| 29525 | 0 | 0 |
| 29550 | 0 | 0 |
| 29575 | 0.01 | 0 |
| 29600 | 0.01 | 0.01 |
| 29625 | 0 | 0 |
| 29650 | 0 | 0 |
| 29675 | 0.01 | 0 |
| 29700 | 0.04 | 0.03 |
| 29725 | 0.01 | 0 |
| 29750 | 0.01 | 0.01 |
| 29775 | 0.01 | 0.01 |
| 29800 | 0.01 | 0 |
| 29825 | 0 | 0 |
| 29850 | 0.02 | 0.01 |
| 29875 | 0 | 0 |
| 29900 | 0.05 | 0.02 |
| 29925 | 0 | 0 |
| 29950 | 0.01 | 0 |
| 29975 | 0.03 | 0.02 |
| 30000 | 2.44 | 1.99 |
| 30025 | 0 | 0 |
| 30050 | 0 | 0 |
| 30075 | 0.01 | 0 |
| 30100 | 0.01 | 0 |
| 30125 | 0.01 | 0 |
| 30150 | 0.01 | 0 |
| 30175 | 0 | 0 |
| 30200 | 0 | 0 |
| 30225 | 0.03 | 0.02 |
| 30250 | 0 | 0 |
| 30275 | 0 | 0 |
| 30300 | 0.01 | 0 |
| 30325 | 0 | 0 |
| 30350 | 0.01 | 0 |
| 30375 | 0.01 | 0.01 |
| 30400 | 0.02 | 0.01 |
| 30425 | 0 | 0 |
| 30450 | 0 | 0 |
| 30475 | 0 | 0 |
| 30500 | 0.01 | 0.01 |
| 30525 | 0 | 0 |
| 30550 | 0 | 0 |
| 30575 | 0.01 | 0 |
| 30600 | 0.01 | 0 |
| 30625 | 0.01 | 0 |
| 30650 | 0 | 0 |
| 30675 | 0 | 0 |
| 30700 | 0.01 | 0 |
| 30725 | 0 | 0 |
| 30750 | 0.03 | 0.02 |
| 30775 | 0 | 0 |
| 30800 | 0.03 | 0.02 |
| 30825 | 0 | 0 |
| 30850 | 0 | 0 |
| 30875 | 0 | 0 |
| 30900 | 0 | 0 |
| 30925 | 0 | 0 |
| 30950 | 0 | 0 |
| 30975 | 0 | 0 |
| 31000 | 0.06 | 0.05 |
| 31025 | 0 | 0 |
| 31050 | 0 | 0 |
| 31075 | 0 | 0 |
| 31100 | 0 | 0 |
| 31125 | 0 | 0 |
| 31150 | 0 | 0 |
| 31175 | 0.01 | 0.01 |
| 31200 | 0.04 | 0.03 |
| 31225 | 0.01 | 0 |
| 31250 | 0 | 0 |
| 31275 | 0.01 | 0 |
| 31300 | 0.03 | 0.02 |
| 31325 | 0 | 0 |
| 31350 | 0 | 0 |
| 31375 | 0 | 0 |
| 31400 | 0 | 0 |
| 31425 | 0 | 0 |
| 31450 | 0.01 | 0.01 |
| 31475 | 0 | 0 |
| 31500 | 0.04 | 0.03 |
| 31525 | 0 | 0 |
| 31550 | 0 | 0 |
| 31575 | 0.01 | 0.01 |
| 31600 | 0 | 0 |
| 31625 | 0 | 0 |
| 31650 | 0 | 0 |
| 31675 | 0.01 | 0 |
| 31700 | 0.01 | 0 |
| 31725 | 0 | 0 |
| 31750 | 0 | 0 |
| 31775 | 0 | 0 |
| 31800 | 0 | 0 |
| 31825 | 0.02 | 0.01 |
| 31850 | 0 | 0 |
| 31875 | 0 | 0 |
| 31900 | 0.01 | 0 |
| 31925 | 0 | 0 |
| 31950 | 0 | 0 |
| 31975 | 0.01 | 0 |
| 32000 | 0.5 | 0.38 |
| 32025 | 0 | 0 |
| 32050 | 0 | 0 |
| 32075 | 0 | 0 |
| 32100 | 0.02 | 0.01 |
| 32125 | 0 | 0 |
| 32150 | 0 | 0 |
| 32175 | 0 | 0 |
| 32200 | 0.02 | 0.01 |
| 32225 | 0.01 | 0.01 |
| 32250 | 0 | 0 |
| 32275 | 0 | 0 |
| 32300 | 0 | 0 |
| 32325 | 0 | 0 |
| 32350 | 0.03 | 0.02 |
| 32375 | 0 | 0 |
| 32400 | 0.03 | 0.02 |
| 32425 | 0 | 0 |
| 32450 | 0 | 0 |
| 32475 | 0 | 0 |
| 32500 | 0.05 | 0.03 |
| 32525 | 0 | 0 |
| 32550 | 0 | 0 |
| 32575 | 0 | 0 |
| 32600 | 0 | 0 |
| 32625 | 0.01 | 0.01 |
| 32650 | 0 | 0 |
| 32675 | 0.01 | 0 |
| 32700 | 0 | 0 |
| 32725 | 0 | 0 |
| 32750 | 0.01 | 0.01 |
| 32775 | 0 | 0 |
| 32800 | 0 | 0 |
| 32825 | 0 | 0 |
| 32850 | 0 | 0 |
| 32875 | 0.01 | 0.01 |
| 32900 | 0 | 0 |
| 32925 | 0 | 0 |
| 32950 | 0.01 | 0.01 |
| 32975 | 0 | 0 |
| 33000 | 0.1 | 0.1 |
| 33025 | 0.01 | 0.01 |
| 33050 | 0 | 0 |
| 33075 | 0 | 0 |
| 33100 | 0.01 | 0 |
| 33125 | 0 | 0 |
| 33150 | 0 | 0 |
| 33175 | 0 | 0 |
| 33200 | 0 | 0 |
| 33225 | 0.01 | 0 |
| 33250 | 0 | 0 |
| 33275 | 0 | 0 |
| 33300 | 0.01 | 0 |
| 33325 | 0 | 0 |
| 33350 | 0 | 0 |
| 33375 | 0 | 0 |
| 33400 | 0 | 0 |
| 33425 | 0.02 | 0.02 |
| 33450 | 0 | 0 |
| 33475 | 0 | 0 |
| 33500 | 0.02 | 0.01 |
| 33525 | 0 | 0 |
| 33550 | 0 | 0 |
| 33575 | 0.02 | 0.01 |
| 33600 | 0.07 | 0.05 |
| 33625 | 0 | 0 |
| 33650 | 0 | 0 |
| 33675 | 0 | 0 |
| 33700 | 0.01 | 0 |
| 33725 | 0 | 0 |
| 33750 | 0.01 | 0 |
| 33775 | 0 | 0 |
| 33800 | 0 | 0 |
| 33825 | 0.01 | 0 |
| 33850 | 0 | 0 |
| 33875 | 0 | 0 |
| 33900 | 0 | 0 |
| 33925 | 0 | 0 |
| 33950 | 0.03 | 0.01 |
| 33975 | 0 | 0 |
| 34000 | 0.06 | 0.06 |
| 34025 | 0.01 | 0 |
| 34050 | 0 | 0 |
| 34075 | 0 | 0 |
| 34100 | 0.01 | 0 |
| 34125 | 0 | 0 |
| 34150 | 0 | 0 |
| 34175 | 0 | 0 |
| 34200 | 0.01 | 0 |
| 34225 | 0 | 0 |
| 34250 | 0 | 0 |
| 34275 | 0 | 0 |
| 34300 | 0.01 | 0 |
| 34325 | 0 | 0 |
| 34350 | 0.01 | 0 |
| 34375 | 0 | 0 |
| 34400 | 0 | 0 |
| 34425 | 0 | 0 |
| 34450 | 0 | 0 |
| 34475 | 0.02 | 0.02 |
| 34500 | 0.02 | 0.01 |
| 34525 | 0 | 0 |
| 34550 | 0.01 | 0.01 |
| 34575 | 0 | 0 |
| 34600 | 0 | 0 |
| 34625 | 0 | 0 |
| 34650 | 0 | 0 |
| 34675 | 0 | 0 |
| 34700 | 0 | 0 |
| 34725 | 0 | 0 |
| 34750 | 0.01 | 0 |
| 34775 | 0 | 0 |
| 34800 | 0.01 | 0.01 |
| 34825 | 0 | 0 |
| 34850 | 0 | 0 |
| 34875 | 0.01 | 0 |
| 34900 | 0 | 0 |
| 34925 | 0 | 0 |
| 34950 | 0 | 0 |
| 34975 | 0 | 0 |
| 35000 | 4.58 | 3.54 |
| 35025 | 0 | 0 |
| 35050 | 0 | 0 |
| 35075 | 0 | 0 |
| 35100 | 0 | 0 |
| 35125 | 0 | 0 |
| 35150 | 0 | 0 |
| 35175 | 0 | 0 |
| 35200 | 0 | 0 |
| 35225 | 0 | 0 |
| 35250 | 0 | 0 |
| 35275 | 0 | 0 |
| 35300 | 0 | 0 |
| 35325 | 0 | 0 |
| 35350 | 0 | 0 |
| 35375 | 0 | 0 |
| 35400 | 0 | 0 |
| 35425 | 0 | 0 |
| 35450 | 0 | 0 |
| 35475 | 0 | 0 |
| 35500 | 0 | 0 |
| 35525 | 0 | 0 |
| 35550 | 0 | 0 |
| 35600 | 0 | 0 |
| 35625 | 0 | 0 |
| 35650 | 0 | 0 |
| 35700 | 0 | 0 |
| 35725 | 0 | 0 |
| 35750 | 0 | 0 |
| 35775 | 0 | 0 |
| 35800 | 0 | 0 |
| 35825 | 0 | 0 |
| 35850 | 0 | 0 |
| 35875 | 0 | 0 |
| 35900 | 0 | 0 |
| 35950 | 0 | 0 |
| 35975 | 0 | 0 |
| 36000 | 0.08 | 0.08 |
| 36050 | 0 | 0 |
| 36075 | 0 | 0 |
| 36100 | 0 | 0 |
| 36125 | 0 | 0 |
| 36150 | 0 | 0 |
| 36175 | 0 | 0 |
| 36200 | 0 | 0 |
| 36225 | 0 | 0 |
| 36250 | 0 | 0 |
| 36275 | 0 | 0 |
| 36300 | 0 | 0 |
| 36325 | 0 | 0 |
| 36350 | 0 | 0 |
| 36375 | 0 | 0 |
| 36400 | 0 | 0 |
| 36425 | 0 | 0 |
| 36450 | 0 | 0 |
| 36475 | 0 | 0 |
| 36500 | 0 | 0 |
| 36525 | 0 | 0 |
| 36550 | 0 | 0 |
| 36575 | 0 | 0 |
| 36600 | 0 | 0 |
| 36625 | 0 | 0 |
| 36650 | 0 | 0 |
| 36675 | 0 | 0 |
| 36700 | 0 | 0 |
| 36725 | 0 | 0 |
| 36750 | 0 | 0 |
| 36775 | 0 | 0 |
| 36800 | 0 | 0 |
| 36825 | 0 | 0 |
| 36850 | 0 | 0 |
| 36875 | 0 | 0 |
| 36900 | 0 | 0 |
| 36950 | 0 | 0 |
| 36975 | 0 | 0 |
| 37000 | 0 | 0.01 |
| 37025 | 0 | 0 |
| 37075 | 0 | 0 |
| 37100 | 0 | 0 |
| 37125 | 0 | 0 |
| 37150 | 0 | 0 |
| 37175 | 0 | 0 |
| 37200 | 0 | 0 |
| 37225 | 0 | 0 |
| 37250 | 0 | 0 |
| 37275 | 0 | 0 |
| 37300 | 0 | 0 |
| 37325 | 0 | 0 |
| 37350 | 0 | 0 |
| 37375 | 0 | 0 |
| 37400 | 0 | 0 |
| 37450 | 0 | 0 |
| 37475 | 0 | 0 |
| 37500 | 0 | 0 |
| 37525 | 0 | 0 |
| 37550 | 0 | 0 |
| 37575 | 0 | 0 |
| 37600 | 0 | 0 |
| 37625 | 0 | 0 |
| 37675 | 0 | 0 |
| 37700 | 0 | 0 |
| 37725 | 0 | 0 |
| 37750 | 0 | 0 |
| 37775 | 0 | 0 |
| 37800 | 0 | 0 |
| 37825 | 0 | 0 |
| 37850 | 0 | 0 |
| 37875 | 0 | 0 |
| 37900 | 0 | 0 |
| 37925 | 0 | 0 |
| 37950 | 0 | 0 |
| 38000 | 0.01 | 0.01 |
| 38025 | 0 | 0 |
| 38050 | 0 | 0 |
| 38075 | 0 | 0 |
| 38100 | 0 | 0 |
| 38125 | 0 | 0 |
| 38150 | 0 | 0 |
| 38175 | 0 | 0 |
| 38200 | 0 | 0 |
| 38225 | 0 | 0 |
| 38250 | 0 | 0 |
| 38275 | 0 | 0 |
| 38300 | 0 | 0 |
| 38325 | 0 | 0 |
| 38350 | 0 | 0 |
| 38375 | 0 | 0 |
| 38400 | 0 | 0 |
| 38425 | 0 | 0 |
| 38450 | 0 | 0 |
| 38475 | 0 | 0 |
| 38500 | 0 | 0 |
| 38525 | 0 | 0 |
| 38550 | 0 | 0 |
| 38600 | 0 | 0 |
| 38625 | 0 | 0 |
| 38650 | 0 | 0 |
| 38675 | 0 | 0 |
| 38700 | 0 | 0 |
| 38725 | 0 | 0 |
| 38750 | 0 | 0 |
| 38775 | 0 | 0 |
| 38800 | 0 | 0 |
| 38825 | 0 | 0 |
| 38850 | 0 | 0 |
| 38875 | 0 | 0 |
| 38900 | 0 | 0 |
| 38925 | 0 | 0 |
| 38950 | 0 | 0 |
| 38975 | 0 | 0 |
| 39000 | 0 | 0 |
| 39075 | 0 | 0 |
| 39100 | 0 | 0 |
| 39125 | 0 | 0 |
| 39150 | 0 | 0 |
| 39175 | 0 | 0 |
| 39200 | 0 | 0 |
| 39250 | 0 | 0 |
| 39275 | 0 | 0 |
| 39325 | 0 | 0 |
| 39350 | 0 | 0 |
| 39400 | 0 | 0 |
| 39475 | 0 | 0 |
| 39500 | 0 | 0 |
| 39525 | 0 | 0 |
| 39550 | 0 | 0 |
| 39600 | 0 | 0 |
| 39625 | 0 | 0 |
| 39650 | 0 | 0 |
| 39675 | 0 | 0 |
| 39700 | 0 | 0 |
| 39750 | 0 | 0 |
| 39775 | 0 | 0 |
| 39800 | 0 | 0 |
| 39825 | 0 | 0 |
| 39850 | 0 | 0 |
| 39900 | 0 | 0 |
| 39950 | 0 | 0 |
| 39975 | 0 | 0 |
| 40000 | 0.42 | 0.4 |
Se observa asociaciones entre los diferentes montos del crédito, es por ello que vamos a categorizar esta variable.
In [27]:
loan['loan_amnt'].describe() #0-40k
buck = [0, 5000, 10000, 15000, 20000, 25000 , 40000]
lab = ['0-5000', '5000-10000', '10000-15000', '15000-20000', '20000-25000','25000+']
loan['loan_amnt_range'] = pd.cut(loan['loan_amnt'], buck, labels=lab)
In [28]:
purp_loan= ['loan_amnt_range', 'credit_risk']
cm = sns.light_palette("purple", as_cmap=True)
(round(pd.crosstab(loan[purp_loan[0]], loan[purp_loan[1]],
normalize='columns') * 100,2)).style.background_gradient(cmap = cm)
Out[28]:
| credit_risk | Default | No Default |
|---|---|---|
| loan_amnt_range | ||
| 0-5000 | 10.82 | 14.31 |
| 5000-10000 | 24.12 | 28.92 |
| 10000-15000 | 22.08 | 20.99 |
| 15000-20000 | 18.03 | 15.36 |
| 20000-25000 | 11.24 | 9.5 |
| 25000+ | 13.71 | 10.92 |
In [29]:
loan.drop(['loan_amnt'],
axis = 1, inplace = True)
Funded Amount & Funded Investor Amount¶
'Funded_amnt' es el monto total comprometido con ese préstamo en ese momento. 'Funded_amnt_inv' es la cantidad total comprometida por los inversores para ese préstamo en ese momento. Cuando no son iguales (el inversor paga menos de lo esperado), lo identificaremos como menos.
In [30]:
loan['amt_difference'] = 'Igual'
loan.loc[(loan['funded_amnt'] - loan['funded_amnt_inv']) > 0,'amt_difference'] = 'Menos'
In [31]:
loan.drop(['funded_amnt','funded_amnt_inv'],1, inplace=True)
Term¶
Term es el número de pagos del préstamo. Los valores están en meses y pueden ser 36 o 60. Se transforman en valores numéricos.
In [32]:
round(loan["term"].isna().sum()/len(loan)*100,2)
Out[32]:
0.0
In [33]:
loan['term'].value_counts()
Out[33]:
36 months 976692 60 months 306451 Name: term, dtype: int64
In [34]:
loan['term'] = loan['term'].apply(lambda s: np.int8(s.split()[0]))
plot_var('term', 'Term', continuous=False)
In [35]:
loan.groupby('term')['credit_risk'].value_counts(normalize=True).loc[:,'Default']
Out[35]:
term 36 0.160491 60 0.326189 Name: credit_risk, dtype: float64
Installment¶
'Installment' es pago mensual adeudado por el prestatario si se otorga el crédito.
In [36]:
plot_var('installment', 'Installment', continuous=True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
In [37]:
loan.groupby('credit_risk')['installment'].describe()
Out[37]:
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| credit_risk | ||||||||
| Default | 256711.0 | 461.698098 | 260.538134 | 15.91 | 273.92 | 400.24 | 599.47 | 1717.63 |
| No Default | 1026432.0 | 428.874358 | 258.758518 | 4.93 | 240.37 | 366.63 | 568.64 | 1719.83 |
Issue Date & Earliest Credit Line¶
'Issue Date' es el mes en que se financió el préstamo y 'Earliest Credit Line' es el mes en que se abrió la primera línea de crédito (informada del prestatario). La información que nos interesa en base a estos datos es la edad crediticia del Prestatario la cual la tenemos guardada en la variable 'mo_sin_old_il_acct' (Meses desde que se abrió la cuenta bancaria más antigua). Debido a ello, extraemos la información de éstas variables.
In [38]:
plot_var('issue_d', 'Issue Date', continuous=False)
In [39]:
from datetime import datetime
loan['issue_year'] = pd.to_datetime(loan.issue_d).dt.year
In [40]:
loan.issue_year.value_counts(normalize=True)
Out[40]:
2015 0.290703 2016 0.210265 2014 0.172598 2017 0.114449 2013 0.105049 2012 0.041591 2018 0.032195 2011 0.016928 2010 0.009771 2009 0.004116 2008 0.001865 2007 0.000470 Name: issue_year, dtype: float64
In [41]:
from datetime import datetime
loan.earliest_cr_line = pd.to_datetime(loan.earliest_cr_line)
dttoday = pd.Timestamp(datetime.now().strftime('%Y-%m-%d'))
loan['earliest_cr_line'].fillna(dttoday, inplace = True)
In [42]:
loan['Age_Borrower'] = loan.earliest_cr_line.apply(lambda x: (0 if (x==dttoday) else (np.timedelta64((x - dttoday),'D')).astype(int))/-365)
plot_var('Age_Borrower', 'Length of of the earliest Credit Line (Months to today)', continuous=True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
In [43]:
loan.drop(['issue_d','earliest_cr_line'],1, inplace=True)
loan.shape
Out[43]:
(1283143, 127)
Payment Plan¶
'Pymnt_plan' indica si se ha establecido un plan de pago para el préstamo. Como se observa en los histogramas, esta variable no tiene capacidad discriminante, es por ello que no la consideramos en el modelo.
In [44]:
plot_var('pymnt_plan', 'Payment Plan', continuous=False)
In [45]:
loan['pymnt_plan'].value_counts(dropna=False)
Out[45]:
n 1283143 Name: pymnt_plan, dtype: int64
In [46]:
loan.drop(['pymnt_plan'],1, inplace=True)
In [47]:
loan.shape
Out[47]:
(1283143, 126)
Desc¶
'Desc' es la descripción del préstamo proporcionada por el prestatario.
In [48]:
loan['desc'].value_counts(dropna=False)
Out[48]:
NaN 1157393
250
Debt Consolidation 13
Borrower added on 03/17/14 > Debt consolidation<br> 11
Borrower added on 02/19/14 > Debt consolidation<br> 9
Borrower added on 03/10/14 > Debt consolidation<br> 9
Camping Membership 8
Borrower added on 01/29/14 > Debt consolidation<br> 8
Borrower added on 01/15/14 > Debt consolidation<br> 7
Borrower added on 01/22/14 > Debt consolidation<br> 7
Borrower added on 02/19/14 > Debt Consolidation<br> 6
Borrower added on 03/07/14 > Debt consolidation<br> 6
Borrower added on 03/18/14 > Debt consolidation<br> 6
Borrower added on 01/03/14 > Debt consolidation<br> 6
Borrower added on 01/27/14 > Debt consolidation<br> 6
Borrower added on 02/26/14 > Debt Consolidation<br> 6
Borrower added on 01/14/13 > Debt consolidation<br> 6
Borrower added on 03/14/14 > Debt consolidation<br> 6
Borrower added on 01/06/14 > Debt consolidation<br> 6
Borrower added on 03/03/14 > Debt consolidation<br> 6
Borrower added on 02/03/14 > Debt consolidation<br> 6
Borrower added on 03/05/14 > debt consolidation<br> 6
Borrower added on 07/25/13 > Debt consolidation<br> 6
Borrower added on 12/19/13 > Debt consolidation<br> 5
Borrower added on 12/10/13 > Debt consolidation<br> 5
Borrower added on 02/25/14 > Debt consolidation.<br> 5
Borrower added on 02/06/14 > Debt consolidation<br> 5
Borrower added on 02/05/14 > Debt consolidation<br> 5
Borrower added on 01/12/13 > Debt Consolidation<br> 5
Borrower added on 09/04/13 > Debt consolidation<br> 5
...
Borrower added on 02/20/14 > My loan is to pay off my credit card so I can pay it offer quicker with the lower interest rate.<br> 1
Borrower added on 11/21/12 > Taking this loan to do improvements to my home including replacing the broken Central A/C unit, replacing the Kitchen appliances and remodeling the master bathroom. I am a responsible borrower & would like to accomplish my home improvement project without falling in the trap of credit cards.<br> 1
Borrower added on 09/12/11 > Just trying to make up the difference and acquire the rest of the loan amount i initially requested. This is for debt consolidation, so anything contributed would be greatly beneficial. Many thanks in advance!<br/> 1
Borrower added on 11/05/13 > payoff 3 credit cards that have a high interest rate<br> 1
Borrower added on 07/07/13 > This loan will enable us to consolidate high interest cards and proceed with the dream of establishing a dance studio in our community specializing in the art of ballet.<br> 1
Borrower added on 11/10/13 > I am paying off three credit cards with a very high interest rate.<br> 1
Borrower added on 12/10/12 > Consolidate my credit cards.<br> 1
Borrower added on 03/17/14 > I am hoping to consolidate my date and plan to use the monthly income that this will free up to step up my retirement planning.<br> 1
I am a mother of 2 who is going to school to be a nurse. I want to consolidate and pay educational expenses, this is a great way to do it. My school loan and Pell grant are due, but do to a hold up at financial aid office it will come after fees are due. Part of this loan will be payed with the expected school money, but some will also be done in payments to build my credit rating. Household gross income is $27700; I also get $158 Supplemental Security income each month for a disability. 1
Borrower added on 01/20/14 > My loan will be used to pay off my credit card debt. I just want to finally be debt free.<br> 1
Borrower added on 11/22/13 > This loan is consolidate all my debt into a nice fixed rate which is a lower rate than what I have today. With the money saved, I hope I can start saving for a down payment on a house. Thank you.<br> 1
Borrower added on 07/18/13 > I would like to pay off some bills, my life would be a lot less stressful to only have one monthly payment .<br> 1
Borrower added on 02/05/13 > Just looking to consolidate a few credit cards and a home improvement loan, while taking advantage of a lower interest rate.<br> 1
Borrower added on 08/16/13 > SOME DEBT CONSOLIDATION<br> 1
Borrower added on 07/18/13 > I am tired of paying a high interest rate on all of my credit cards. I will use the loan to pay off all of my credit cards (thus increasing my credit score), combine the payments into one, and use the money I am saving in interest towards savings for the future.<br> 1
Borrower added on 07/23/12 > This loan is for debt consolidation of higher interest debts (Credit Cards). I automate my payments but this will still save me money in the long run.<br> 1
Borrower added on 11/04/13 > Had some financial difficulties with my aging mother who is 2500 miles away so we were using our AMEX to travel back and forth and pay for necessities. We would like to eliminate our credit card balance and have a plan to pay off the money more efficiently.<br> 1
Borrower added on 08/08/12 > Tell your story. What is your loan for?<br><br> Borrower added on 08/08/12 > Time to consoldiate debt into one payment.<br> 1
Borrower added on 04/01/13 > i want to do only one payment a month for my credit cards .<br> 1
Borrower added on 10/29/12 > This loan is to consolidate my credit card debt to one source.<br> 1
Borrower added on 03/11/13 > Loan proceeds will be used to consolidate remaining credit card debt.<br> 1
Borrower added on 04/21/12 > Much needed home improvement. Safer living equals better life.<br><br> Borrower added on 04/23/12 > I need to add metal roofing, as its has 40 year warranty.<br><br> Borrower added on 04/23/12 > I have excellent credit history. I will pay back 100% of loan. I have never not paid a loan off. I have 15 years of sinority, so my job is secure.<br><br> Borrower added on 04/23/12 > I will answer any questions. I have to get this loan, as banks want way to much interest, and want me to morgage my owned home for 7000.<br><br> Borrower added on 04/24/12 > I own my home, my truck, and currently have no big payments monthly. Been working for 15 years at the same place and have sinority, so losing my job would never happen. If i am fully fundedbi can do what i need to do for my home and family.<br><br> Borrower added on 04/27/12 > Need more investors, you will not be dissapointed in this loan. You have my word, my credit score of 775 should say it all.<br><br> Borrower added on 04/30/12 > 5 days left come onl. I need this to put a new roof on my house, Please invest you will be 100% rewarded, because if any of you can see my credit report, i have never not paid a loan. I have paid off 3 trucks and classic car, and my home in my adult working life.<br> 1
Borrower added on 12/27/13 > To consolidate credit card debt.<br> 1
I plan to use this money to refinance about $9000+ that I am currently paying 18.99% on and paying for my last semester of school costs (I went back to school for my BA 2-years ago and graduate next month). I have never missed or been late on a payment (the delinquency from 49-months ago was a disputed mistake) and my income is good. For clarification of my revolving credit balance, $95K of that is actually a second mortgage which is amortized at a fixed rate over 15 years. Another $17K is on another card, but at a much more manageable rate of 6.99%. Though my payments are manageable I could really use this loan to gain some momentum in repaying my debt quicker. 1
Borrower added on 10/03/13 > Loan is for home improvements including tile, carpet, flooring and several structural improvements. Some are completed and on charge cards, some still in progress. Loan will consolidate all expenses into one payment. Intent is to be in this home long term.<br><br> Borrower added on 10/03/13 > Loan is to consolidate home improvement expenses into one payment. I have always been a reliable borrower, having no late payments in over 6 years and that being only a few days due to oversight. My employment is stable at an expanding automotive marketing firm, and pay raise expected this month.<br> 1
Borrower added on 12/09/13 > we would like to purchase a honda trike<br> 1
Borrower added on 11/25/13 > Need to consolidate and eliminate old debt.<br> 1
Borrower added on 11/14/13 > With this loan I will be paying off the balance of my current lending club loan of 8800.00 and paying off a credit card. I have had the current loan for approx. 3 years and never late. Once completed I will have eliminated 200.00 worth of monthly payments.<br> 1
I am a certified independent Apple Technology Consultant who is referred by several local retail stores to companies in need of technical support (Apple employees can’t go to customer locations or troubleshoot third-party software or hardware.) I have been in business for five years and in related fields for ten more. My specialties include the maintenance of computer networks, internet access, and email systems; software and hardware troubleshooting; data backups; and data recovery from damaged hard drives. I also utilize the latest technology for remote troubleshooting and can take control of client computers within seconds of a request. I am very technically informed and receive daily several hundred confidential internal emails within the Apple Consultants Network that inform me of up-to-the-minute problems that other consultants are encountering at customer locations throughout the U.S. I also monitor a dozen third-party technical sites covering countless issues as they occur. For the last five years I have been going solo and have been too busy to take on new customers, but an opportunity has presented itself to take on someone with whom I’ve worked in the past who has the perfect technical expertise. Toward this end I’ve just purchased another cargo van (that I will turn into a mobile information technology center) and I’d started to buy equipment using some special-rate credit card offers when I realized that—as silly as this may sound, as I just signed up to be a lender—I can borrow at a better rate over a longer period of time from LendingClub.com. I still need to purchase a laptop, desktop, diagnostic software, hardware, communication equipment, storage racks for the van, switches, routers, and network cabling for inventory as well as certification classes for my new employee. I also read many local, regional, and international newspapers daily, uncovering gems like LendingClub.com and recently noticed they have a program to trade promissory notes with FolioFN.com—another brilliant investment vehicle. LendingClub.com is a great concept to allow the little guy (lender or borrower) to participate like the big guys. 1
Borrower added on 11/14/13 > THIS LOAN IS TO PAY OFF CREDIT DEBT FASTER.<br> 1
Name: desc, Length: 124204, dtype: int64
Purpose¶
'Purpose' es el próposito del crédito proporcionada por el prestatario para la solicitud de préstamo.
In [49]:
plot_var('purpose', 'purpose', continuous=False)
In [50]:
purp_loan= ['purpose', 'credit_risk']
cm = sns.light_palette("purple", as_cmap=True)
(round(pd.crosstab(loan[purp_loan[1]], loan[purp_loan[0]],
normalize='columns') * 100,2)).style.background_gradient(cmap = cm)
Out[50]:
| purpose | car | credit_card | debt_consolidation | educational | home_improvement | house | major_purchase | medical | moving | other | renewable_energy | small_business | vacation | wedding |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| credit_risk | ||||||||||||||
| Default | 14.6 | 17.01 | 21.17 | 20.8 | 17.86 | 21.78 | 18.66 | 21.7 | 23.12 | 21.1 | 23.69 | 29.73 | 19.18 | 12.43 |
| No Default | 85.4 | 82.99 | 78.83 | 79.2 | 82.14 | 78.22 | 81.34 | 78.3 | 76.88 | 78.9 | 76.31 | 70.27 | 80.82 | 87.57 |
In [51]:
loan.loc[(loan['purpose'] == 'debt_consolidation')|(loan['purpose'] =="credit_card"), 'purpose_g'] = 'debt'
loan.loc[(loan['purpose'] == 'home_improvement')|(loan['purpose'] =="major_purchase")|
(loan['purpose'] == 'car')|(loan['purpose'] =="house")|
(loan['purpose'] == 'vacation')|(loan['purpose'] =="renewable_energy"),
'purpose_g'] = 'major_purchase'
loan.loc[(loan['purpose'] == 'small_business')|(loan['purpose'] =="medical")|
(loan['purpose'] == 'moving')|(loan['purpose'] =="wedding")|
(loan['purpose'] == 'educational'),
'purpose_g'] = 'life_event'
loan.loc[(loan['purpose'] == 'other'), 'purpose_g'] = 'other'
In [52]:
loan.groupby('purpose_g')['credit_risk'].value_counts(normalize=True)[:,'Default'].sort_values(ascending=False)
Out[52]:
purpose_g life_event 0.243714 other 0.210980 debt 0.200273 major_purchase 0.180022 Name: credit_risk, dtype: float64
In [53]:
plot_var('purpose_g', 'purpose', continuous=False)
In [54]:
loan.drop('purpose', axis=1, inplace=True)
loan.select_dtypes(include=['object']).columns
Out[54]:
Index(['grade', 'sub_grade', 'emp_title', 'emp_length', 'home_ownership',
'verification_status', 'loan_status', 'desc', 'title', 'zip_code',
'addr_state', 'initial_list_status', 'last_pymnt_d', 'next_pymnt_d',
'last_credit_pull_d', 'hardship_flag', 'hardship_type',
'hardship_reason', 'hardship_status', 'hardship_start_date',
'hardship_end_date', 'payment_plan_start_date', 'hardship_loan_status',
'disbursement_method', 'debt_settlement_flag',
'debt_settlement_flag_date', 'settlement_status', 'settlement_date',
'credit_risk', 'amt_difference', 'purpose_g'],
dtype='object')
Title¶
'Title' es el título del préstamo proporcionado por el prestatario.
In [55]:
loan['title'].describe()
Out[55]:
count 1268277 unique 63148 top Debt consolidation freq 624905 Name: title, dtype: object
In [56]:
loan['title'].value_counts().head(10)
Out[56]:
Debt consolidation 624905 Credit card refinancing 236816 Home improvement 70345 Other 62604 Major purchase 22552 Debt Consolidation 15760 Medical expenses 12653 Business 11153 Car financing 10582 Vacation 7603 Name: title, dtype: int64
Policy Code¶
'Policy_code' indica que código de política se utiliza para la gestión del préstamo. Si es 1 esta disponible públicamente, en cambio 2 es cuando la información de los nuevos productos no esta disponible públicamente. Según el análisis exploratorio de esta información se tiene sólo el código 1, es asi que no la consideraremos para el modelo debido a que no tiene capacidad discriminante.
In [57]:
loan['policy_code'].value_counts()
Out[57]:
1 1283143 Name: policy_code, dtype: int64
In [58]:
plot_var('policy_code','Policy Code', continuous=False)
In [59]:
loan.drop(['policy_code'],1, inplace=True)
loan.shape
Out[59]:
(1283143, 125)
Initial List Status¶
In [60]:
loan['initial_list_status'].value_counts()
Out[60]:
w 732685 f 550458 Name: initial_list_status, dtype: int64
In [61]:
plot_var('initial_list_status','Initial list status', continuous=False)
Caracteristicas del Prestatario¶
En este apartado se agruparan las variables que hacen referencia al prestatario. Estas variables son:
- 'Job Title': job title supplied by the Borrower when applying for the loan.
- 'Employment Length': Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years.
- 'Home Ownership': The home ownership status provided by the borrower during registration or obtained from the credit report. Our values are: RENT, OWN, MORTGAGE, OTHER.
- 'Annual Income': The self-reported annual income provided by the borrower during registration.
- 'Verification Status': Indicates if income was verified by LC, not verified, or if the income source was verified.
- 'Zip Code': The first 3 numbers of the zip code provided by the borrower in the loan application.
- 'Address State': The state provided by the borrower in the loan application.
Employment Length¶
In [62]:
loan['emp_length'].value_counts(dropna=False).sort_index()
Out[62]:
1 year 84945 10+ years 423148 2 years 116481 3 years 103042 4 years 77106 5 years 80711 6 years 60339 7 years 57610 8 years 58604 9 years 49030 < 1 year 99984 NaN 72143 Name: emp_length, dtype: int64
In [63]:
loan['emp_length'].replace('10+ years', '10 years', inplace=True)
loan['emp_length'].replace('< 1 year', '0 years', inplace=True)
loan.emp_length.map( lambda x: str(x).split()[0]).value_counts(dropna=True).sort_index()
loan['emp_length'] = loan.emp_length.map( lambda x: float(str(x).split()[0]))
plot_var('emp_length', 'Employment length', continuous=False)
In [64]:
loan.emp_length.fillna(value=0,inplace=True)
drop_list = ['emp_title','desc','title']
loan.drop(labels=drop_list, axis=1, inplace=True)
Home Ownership¶
In [65]:
loan['home_ownership'].value_counts()
Out[65]:
MORTGAGE 630985 RENT 513799 OWN 137859 ANY 267 OTHER 182 NONE 51 Name: home_ownership, dtype: int64
In [66]:
loan['home_ownership'].replace(['NONE','ANY'],'OTHER', inplace=True)
plot_var('home_ownership', 'Home Ownership', continuous=False)
In [67]:
purp_loan= ['home_ownership', 'credit_risk']
cm = sns.light_palette("purple", as_cmap=True)
(round(pd.crosstab(loan[purp_loan[0]], loan[purp_loan[1]],
normalize='columns') * 100,2)).style.background_gradient(cmap = cm)
Out[67]:
| credit_risk | Default | No Default |
|---|---|---|
| home_ownership | ||
| MORTGAGE | 42.36 | 50.88 |
| OTHER | 0.04 | 0.04 |
| OWN | 11.07 | 10.66 |
| RENT | 46.54 | 38.42 |
Annual Income¶
In [68]:
loan['annual_inc'] = loan['annual_inc'].apply(lambda x:np.log10(x+1))
loan['annual_inc'].describe()
Out[68]:
count 1.283139e+06 mean 4.816080e+00 std 2.323322e-01 min 3.278067e+00 25% 4.662767e+00 50% 4.812920e+00 75% 4.954247e+00 max 7.041361e+00 Name: annual_inc, dtype: float64
In [69]:
loan['annual_inc'].fillna(0, inplace = True)
In [70]:
plot_var('annual_inc', 'Log10 Annual income', continuous=True);
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Verification Status¶
In [71]:
loan['verification_status'].value_counts()
Out[71]:
Source Verified 497395 Verified 399200 Not Verified 386548 Name: verification_status, dtype: int64
In [72]:
plot_var('verification_status', 'Verification Status', continuous=False)
In [73]:
purp_loan= ['verification_status', 'credit_risk']
cm = sns.light_palette("purple", as_cmap=True)
(round(pd.crosstab(loan[purp_loan[0]], loan[purp_loan[1]],
normalize='columns') * 100,2)).style.background_gradient(cmap = cm)
Out[73]:
| credit_risk | Default | No Default |
|---|---|---|
| verification_status | ||
| Not Verified | 22.09 | 32.14 |
| Source Verified | 40.82 | 38.25 |
| Verified | 37.09 | 29.61 |
In [74]:
loan['verification_status'].replace(['Source Verified','Verified'],'Verified', inplace=True)
plot_var('verification_status', 'Estado Verificación', continuous=False)
In [75]:
di = {"Verified":1, "No Verified":0} #converting target variable to boolean
loan = loan.replace({"verification_status": di})
Address State & Zip Code¶
In [76]:
loan['zip_code']=loan['zip_code'].replace(regex=['xx'], value='')
In [77]:
sns.countplot(loan['addr_state'],hue=loan['credit_risk'])
Out[77]:
<matplotlib.axes._subplots.AxesSubplot at 0x167fef87d68>
In [78]:
# create region of residence based on state
west = ['CA', 'OR', 'UT','WA', 'CO', 'NV', 'AK', 'MT', 'HI', 'WY', 'ID']
south_west = ['AZ', 'TX', 'NM', 'OK']
south_east = ['GA', 'NC', 'VA', 'FL', 'KY', 'SC', 'LA', 'AL', 'WV', 'DC', 'AR', 'DE', 'MS', 'TN' ]
mid_west = ['IL', 'MO', 'MN', 'OH', 'WI', 'KS', 'MI', 'SD', 'IA', 'NE', 'IN', 'ND']
north_east = ['CT', 'NY', 'PA', 'NJ', 'RI','MA', 'MD', 'VT', 'NH', 'ME']
def finding_regions(state):
if state in west:
return 'West'
elif state in south_west:
return 'SouthWest'
elif state in south_east:
return 'SouthEast'
elif state in mid_west:
return 'MidWest'
elif state in north_east:
return 'NorthEast'
In [79]:
loan['region'] = loan['addr_state'].apply(finding_regions)
loan['region'].isnull().sum()
Out[79]:
0
In [80]:
plot_var('region', 'US Region', continuous=False)
In [81]:
loan.drop(['addr_state','zip_code'],
axis = 1, inplace = True)
Evaluación del Prestatario¶
En este apartado se agruparan las variables que hacen referencia a información resultante de la evaluación del prestatario. Estas variables son:
- 'Interest Rate': Interest Rate on the loan.
- 'Grade': LC assigned loan grade.
- 'Sub Grade': LC assigned loan subgrade.
Interest Rate¶
In [82]:
plot_var('int_rate', 'Interest Rate', continuous=True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
In [83]:
order_sub = loan.groupby("grade")['int_rate'].count().index
g2 = sns.boxenplot(x="grade", y="int_rate", data=loan,
palette="hls", order=order_sub)
g2.set_xlabel("Grade Values", fontsize=15)
g2.set_ylabel("Interest Rate", fontsize=15)
g2.set_title("Lending Club Loan - Interest Rate Distribution by Sub-Grade", fontsize=20)
plt.subplots_adjust(hspace = 0.4,top = 0.9)
plt.show()
Grade y Sub Grade¶
Convertimos esta variable en valores numéricos, según el grado de riesgo del préstamo, a fin de reducir la complejidad del modelo. Prescindiremos de la variable 'subgrade' por considerar que contiene información reiterativa.
In [84]:
grade_mapping={'A':7,'B':6,'C':5,'D':4,'E':3,'F':2,'G':1}
loan["grade"] = loan["grade"].replace(grade_mapping)
In [85]:
loan.drop(['sub_grade'],axis = 1, inplace = True)
In [86]:
plot_var('grade','Grade',continuous=False)
In [87]:
purp_loan= ['credit_risk','grade']
cm = sns.light_palette("purple", as_cmap=True)
(round(pd.crosstab(loan[purp_loan[0]], loan[purp_loan[1]],
normalize='columns') * 100,2)).style.background_gradient(cmap = cm)
Out[87]:
| grade | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| credit_risk | |||||||
| Default | 49.64 | 45.21 | 38.53 | 30.38 | 22.44 | 13.41 | 6.04 |
| No Default | 50.36 | 54.79 | 61.47 | 69.62 | 77.56 | 86.59 | 93.96 |
Historial Crediticio del Prestatario¶
Bajo este epígrafe agruparemos todas las variables que describen el historial crediticio del usuario.
- 'delinq_2yrs' : The number of 30+ days past-due incidences of delinquency in the borrower's credit file for the past 2 years.
- 'inq_last_6mths' : The number of inquiries in past 6 months (excluding auto and mortgage inquiries).
- 'mths_since_last_delinq' : The number of months since the borrower's last delinquency.
- 'mths_since_last_record' : The number of months since the last public record.
- 'pub_rec' : Number of derogatory public records.
- 'collections_12_mths_ex_med' : Number of collections in 12 months excluding medical collections.
- 'mths_since_last_major_derog' : Months since most recent 90-day or worse rating.
- 'tot_coll_amt' : Total collection amounts ever owed.
- 'open_acc_6m' : Number of open trades in last 6 months.
- 'open_act_il' : Number of currently active installment trades.
- 'open_il_12m' : Number of installment accounts opened in past 12 months.
- 'open_il_24m' : Number of installment accounts opened in past 24 months.
- 'mths_since_rcnt_il' : Months since most recent installment accounts opened.
- 'open_rv_12m' : Number of revolving trades opened in past 12 months.
- 'open_rv_24m' : Number of revolving trades opened in past 24 months.
- 'inq_fi' : Number of personal finance inquiries.
- 'inq_last_12m' : Number of credit inquiries in past 12 months.
- 'acc_open_past_24mths' : Number of trades opened in past 24 months.
- 'chargeoff_within_12_mths' : Number of charge-offs within 12 months.
- 'mo_sin_old_il_acct' : Months since oldest bank installment account opened.
- 'mo_sin_old_rev_tl_op' : Months since oldest revolving account opened.
- 'mo_sin_rcnt_rev_tl_op' : Months since most recent revolving account opened.
- 'mo_sin_rcnt_tl' : Months since most recent account opened.
- 'num_accts_ever_120_pd' : Number of accounts ever 120 or more days past due.
- 'num_actv_bc_tl' : Number of currently active bankcard accounts.
- 'num_actv_rev_tl' : Number of currently active revolving trades.
- 'num_bc_sats' : Number of satisfactory bankcard accounts.
- 'num_bc_tl' : Number of bankcard accounts.
- 'num_il_tl' : Number of installment accounts.
- 'num_op_rev_tl' : Number of open revolving accounts.
- 'num_rev_accts' : Number of revolving accounts.
- 'num_rev_tl_bal_gt_0' : Number of revolving trades with balance >0.
- 'num_sats' : Number of satisfactory accounts.
- 'num_tl_120dpd_2m' : Number of accounts currently 120 days past due (updated in past 2 months).
- 'num_tl_30dpd' : Number of accounts currently 30 days past due (updated in past 2 months).
- 'num_tl_90g_dpd_24m' : Number of accounts 90 or more days past due in last 24 months.
- 'num_tl_op_past_12m' : Number of accounts opened in past 12 months.
- 'pct_tl_nvr_dlq' : Percent of trades never delinquent.
- 'percent_bc_gt_75' : Percentage of all bankcard accounts > 75% of limit.
- 'pub_rec_bankruptcies' : Number of public record bankruptcies.
- 'tax_liens' : Number of tax liens.
- 'tot_hi_cred_lim' : Total high credit/credit limit.
- 'total_bal_ex_mort' : Total credit balance excluding mortgage.
- 'total_bc_limit' : Total bankcard high credit/credit limit.
- 'total_il_high_credit_limit' : Total installment high credit/credit limit.
En primer lugar, calculamos correlaciones entre las variables. Como se puede observar, existe una gran correlación entre variables, porque recoger información parecida. En este sentido, se decide conservar una variable representativa de la información, y prescindir del resto.
In [88]:
matrix_corr(loan[['delinq_2yrs', 'inq_last_6mths', 'mths_since_last_delinq',
'mths_since_last_record', 'pub_rec', 'collections_12_mths_ex_med',
'mths_since_last_major_derog','tot_coll_amt',
'open_acc_6m','open_act_il','open_il_12m','open_il_24m',
'mths_since_rcnt_il','open_rv_12m','open_rv_24m',
'inq_fi','inq_last_12m','acc_open_past_24mths',
'chargeoff_within_12_mths','mo_sin_old_il_acct',
'mo_sin_old_rev_tl_op','mo_sin_rcnt_rev_tl_op',
'mo_sin_rcnt_tl']], mirror=False)
In [89]:
matrix_corr(loan[['num_accts_ever_120_pd', 'num_actv_bc_tl', 'num_actv_rev_tl',
'num_bc_sats', 'num_bc_tl', 'num_il_tl', 'num_op_rev_tl',
'num_rev_accts', 'num_rev_tl_bal_gt_0', 'num_sats', 'num_tl_120dpd_2m',
'num_tl_30dpd', 'num_tl_90g_dpd_24m', 'num_tl_op_past_12m',
'pct_tl_nvr_dlq', 'percent_bc_gt_75', 'pub_rec_bankruptcies',
'tax_liens', 'tot_hi_cred_lim', 'total_bal_ex_mort', 'total_bc_limit',
'total_il_high_credit_limit']], mirror=False)
Para eliminar redundancias en cuanto a número de cuentas, seleccionamos la variable 'Number of satisfactory accounts' (num_sats) y eliminamos las variables que contienen información similar:
In [90]:
loan.drop(['num_actv_rev_tl','num_actv_bc_tl','num_bc_sats','num_bc_tl','num_op_rev_tl','num_rev_accts','num_rev_tl_bal_gt_0'],
axis = 1, inplace = True)
In [91]:
loan.shape
Out[91]:
(1283143, 113)
Deliquency in last 2 years¶
El número de más de 30 días de incidencias vencidas de morosidad en el archivo de crédito del prestatario durante los últimos 2 años.
In [92]:
loan['delinq_2yrs'].isnull().sum()
Out[92]:
29
Las observaciones ausentes significan que no los individuos no han tenido episodios de morosidad durante los últimos 2 años, es así que los replazamos por valor 0.
In [93]:
loan['delinq_2yrs'].fillna(0, inplace = True)
In [94]:
plot_var('delinq_2yrs', 'Delinquency in last 2 years',
continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Para facilitar el análisis del modelo, por que el 80% de casos (Default y No Default) es valor 0 (Por tener mucho bias el grafico de boxplot lo identifica como outliers), se convertirá esta variable como un indicador si el prestatario tuvo un episodio de morosidad durante los últimos 2 años. (0 que no tiene, y 1 que si tuvo por lo menos un episodio)
In [95]:
purp_loan= ['delinq_2yrs', 'credit_risk']
cm = sns.light_palette("purple", as_cmap=True)
(round(pd.crosstab(loan[purp_loan[0]], loan[purp_loan[1]],
normalize='columns') * 100,2)).style.background_gradient(cmap = cm)
Out[95]:
| credit_risk | Default | No Default |
|---|---|---|
| delinq_2yrs | ||
| 0.0 | 79.26 | 81.1 |
| 1.0 | 13.38 | 12.66 |
| 2.0 | 4.19 | 3.64 |
| 3.0 | 1.58 | 1.31 |
| 4.0 | 0.71 | 0.58 |
| 5.0 | 0.37 | 0.3 |
| 6.0 | 0.2 | 0.16 |
| 7.0 | 0.12 | 0.09 |
| 8.0 | 0.06 | 0.05 |
| 9.0 | 0.04 | 0.03 |
| 10.0 | 0.03 | 0.02 |
| 11.0 | 0.02 | 0.01 |
| 12.0 | 0.02 | 0.01 |
| 13.0 | 0.01 | 0.01 |
| 14.0 | 0.01 | 0.01 |
| 15.0 | 0 | 0 |
| 16.0 | 0 | 0 |
| 17.0 | 0 | 0 |
| 18.0 | 0 | 0 |
| 19.0 | 0 | 0 |
| 20.0 | 0 | 0 |
| 21.0 | 0 | 0 |
| 22.0 | 0 | 0 |
| 24.0 | 0 | 0 |
| 25.0 | 0 | 0 |
| 26.0 | 0 | 0 |
| 27.0 | 0 | 0 |
| 29.0 | 0 | 0 |
| 30.0 | 0 | 0 |
| 39.0 | 0 | 0 |
In [96]:
loan["delinq_2yrs_cat"] = 0
loan.loc[loan["delinq_2yrs"] > 0, "delinq_2yrs_cat"] = 1
In [97]:
purp_loan= ['delinq_2yrs_cat', 'credit_risk']
cm = sns.light_palette("purple", as_cmap=True)
(round(pd.crosstab(loan[purp_loan[0]], loan[purp_loan[1]],
normalize='columns') * 100,2)).style.background_gradient(cmap = cm)
Out[97]:
| credit_risk | Default | No Default |
|---|---|---|
| delinq_2yrs_cat | ||
| 0 | 79.26 | 81.1 |
| 1 | 20.74 | 18.9 |
In [98]:
loan.drop(['delinq_2yrs'],
axis = 1, inplace = True)
Inquires Last 6 Months¶
Una 'inquiry' es una comprobación que realiza Lending Club sobre las operaciones realizadas por el prestatario. Estas pueden ser 'soft' (en operaciones menores, que no afectan al credit score) o 'hard' (en operaciones de mayor riesgo, como hipotecas o líneas de crédito extra). Las soft inquiries no afectan al credit score. Por el contrario, una hard inquiry puede tener un mínimo impacto sobre el credit score del prestamista (1 a 5 puntos). Sólo la acumulación de hard inquiries tienen un impacto real.
https://help.lendingclub.com/hc/en-us/articles/213803238-Understanding-your-credit-score
Dado que esta variable no distingue entre soft y hard inquiries, consideramos que la inclusión de esta variable podría distorsionar el resultado, por lo que se decide prescindir de ella.
In [99]:
loan.drop(['inq_last_6mths'],
axis = 1, inplace = True)
Number of derogatory public records¶
'pub rec' guarda el número de registros públicos despectivos. Los casos con valores NaN significa que el prestatario no tiene registros públicos y se imputarán a 0.
In [100]:
loan['pub_rec'].isnull().sum()
Out[100]:
29
In [101]:
loan['pub_rec'].fillna(0, inplace = True)
In [102]:
plot_var('pub_rec', 'Derogatory public records',
continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
In [103]:
purp_loan= ['pub_rec', 'credit_risk']
cm = sns.light_palette("purple", as_cmap=True)
(round(pd.crosstab(loan[purp_loan[0]], loan[purp_loan[1]],
normalize='columns') * 100,2)).style.background_gradient(cmap = cm)
Out[103]:
| credit_risk | Default | No Default |
|---|---|---|
| pub_rec | ||
| 0.0 | 80.7 | 83.68 |
| 1.0 | 16.03 | 13.68 |
| 2.0 | 2.17 | 1.74 |
| 3.0 | 0.63 | 0.53 |
| 4.0 | 0.24 | 0.18 |
| 5.0 | 0.11 | 0.09 |
| 6.0 | 0.06 | 0.05 |
| 7.0 | 0.02 | 0.02 |
| 8.0 | 0.01 | 0.01 |
| 9.0 | 0.01 | 0.01 |
| 10.0 | 0 | 0 |
| 11.0 | 0 | 0 |
| 12.0 | 0 | 0 |
| 13.0 | 0 | 0 |
| 14.0 | 0 | 0 |
| 15.0 | 0 | 0 |
| 16.0 | 0 | 0 |
| 17.0 | 0 | 0 |
| 18.0 | 0 | 0 |
| 19.0 | 0 | 0 |
| 20.0 | 0 | 0 |
| 21.0 | 0 | 0 |
| 22.0 | 0 | 0 |
| 23.0 | 0 | 0 |
| 24.0 | 0 | 0 |
| 25.0 | 0 | 0 |
| 28.0 | 0 | 0 |
| 34.0 | 0 | 0 |
| 37.0 | 0 | 0 |
| 40.0 | 0 | 0 |
| 46.0 | 0 | 0 |
| 47.0 | 0 | 0 |
| 49.0 | 0 | 0 |
| 54.0 | 0 | 0 |
| 61.0 | 0 | 0 |
| 63.0 | 0 | 0 |
| 86.0 | 0 | 0 |
Se observa que alrededor del 80% de casos (Default y No Default) tiene valor 0 (Por tener mucho bias el grafico de boxplot lo identifica como outliers). De forma similar a la variable Delinq 2 yrs, se convertirá esta variable como un indicador si el prestatario tiene al menos un registro público.
In [104]:
loan["pub_rec_cat"] = 0
loan.loc[loan["pub_rec"]>0,"pub_rec_cat"] = 1
In [105]:
purp_loan= ['pub_rec_cat', 'credit_risk']
cm = sns.light_palette("purple", as_cmap=True)
(round(pd.crosstab(loan[purp_loan[0]], loan[purp_loan[1]],
normalize='columns') * 100,2)).style.background_gradient(cmap = cm)
Out[105]:
| credit_risk | Default | No Default |
|---|---|---|
| pub_rec_cat | ||
| 0 | 80.7 | 83.68 |
| 1 | 19.3 | 16.32 |
In [106]:
loan.drop(['pub_rec'],
axis = 1, inplace = True)
Months since last deliquency¶
El número de meses desde la última morosidad del prestatario. Las observaciones ausentes significan que los individuos no han tenido episodios de morosidad, es así que los replazamos por valor 0.
In [107]:
loan['mths_since_last_delinq'].isnull().sum()
Out[107]:
647006
In [108]:
loan['mths_since_last_delinq'].fillna(0, inplace = True)
In [109]:
plot_var('mths_since_last_delinq', 'Months since last deliquency',
continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
In [110]:
purp_loan= ['mths_since_last_delinq', 'credit_risk']
cm = sns.light_palette("purple", as_cmap=True)
(round(pd.crosstab(loan[purp_loan[0]], loan[purp_loan[1]],
normalize='columns') * 100,2)).style.background_gradient(cmap = cm)
Out[110]:
| credit_risk | Default | No Default |
|---|---|---|
| mths_since_last_delinq | ||
| 0.0 | 48.81 | 51.03 |
| 1.0 | 0.4 | 0.36 |
| 2.0 | 0.54 | 0.46 |
| 3.0 | 0.64 | 0.57 |
| 4.0 | 0.77 | 0.67 |
| 5.0 | 0.88 | 0.78 |
| 6.0 | 1.07 | 0.91 |
| 7.0 | 1.01 | 0.9 |
| 8.0 | 0.98 | 0.9 |
| 9.0 | 1.02 | 0.92 |
| 10.0 | 0.95 | 0.88 |
| 11.0 | 0.92 | 0.86 |
| 12.0 | 1.02 | 0.93 |
| 13.0 | 1.03 | 0.92 |
| 14.0 | 0.97 | 0.9 |
| 15.0 | 0.99 | 0.88 |
| 16.0 | 0.89 | 0.84 |
| 17.0 | 0.9 | 0.83 |
| 18.0 | 0.89 | 0.84 |
| 19.0 | 0.86 | 0.84 |
| 20.0 | 0.82 | 0.81 |
| 21.0 | 0.85 | 0.82 |
| 22.0 | 0.85 | 0.8 |
| 23.0 | 0.8 | 0.79 |
| 24.0 | 0.81 | 0.79 |
| 25.0 | 0.83 | 0.8 |
| 26.0 | 0.86 | 0.8 |
| 27.0 | 0.81 | 0.76 |
| 28.0 | 0.8 | 0.78 |
| 29.0 | 0.8 | 0.75 |
| 30.0 | 0.78 | 0.74 |
| 31.0 | 0.76 | 0.74 |
| 32.0 | 0.73 | 0.71 |
| 33.0 | 0.73 | 0.71 |
| 34.0 | 0.72 | 0.71 |
| 35.0 | 0.68 | 0.71 |
| 36.0 | 0.7 | 0.7 |
| 37.0 | 0.7 | 0.69 |
| 38.0 | 0.7 | 0.68 |
| 39.0 | 0.67 | 0.67 |
| 40.0 | 0.66 | 0.67 |
| 41.0 | 0.65 | 0.65 |
| 42.0 | 0.65 | 0.65 |
| 43.0 | 0.64 | 0.64 |
| 44.0 | 0.63 | 0.64 |
| 45.0 | 0.63 | 0.63 |
| 46.0 | 0.61 | 0.61 |
| 47.0 | 0.6 | 0.6 |
| 48.0 | 0.58 | 0.6 |
| 49.0 | 0.53 | 0.53 |
| 50.0 | 0.43 | 0.44 |
| 51.0 | 0.46 | 0.42 |
| 52.0 | 0.43 | 0.42 |
| 53.0 | 0.43 | 0.43 |
| 54.0 | 0.46 | 0.43 |
| 55.0 | 0.43 | 0.42 |
| 56.0 | 0.43 | 0.43 |
| 57.0 | 0.43 | 0.43 |
| 58.0 | 0.43 | 0.42 |
| 59.0 | 0.43 | 0.41 |
| 60.0 | 0.42 | 0.42 |
| 61.0 | 0.44 | 0.42 |
| 62.0 | 0.43 | 0.41 |
| 63.0 | 0.43 | 0.4 |
| 64.0 | 0.4 | 0.41 |
| 65.0 | 0.42 | 0.4 |
| 66.0 | 0.42 | 0.4 |
| 67.0 | 0.4 | 0.41 |
| 68.0 | 0.42 | 0.4 |
| 69.0 | 0.41 | 0.38 |
| 70.0 | 0.42 | 0.39 |
| 71.0 | 0.38 | 0.37 |
| 72.0 | 0.37 | 0.37 |
| 73.0 | 0.37 | 0.38 |
| 74.0 | 0.37 | 0.36 |
| 75.0 | 0.35 | 0.36 |
| 76.0 | 0.34 | 0.34 |
| 77.0 | 0.31 | 0.32 |
| 78.0 | 0.3 | 0.31 |
| 79.0 | 0.3 | 0.3 |
| 80.0 | 0.28 | 0.29 |
| 81.0 | 0.27 | 0.26 |
| 82.0 | 0.11 | 0.11 |
| 83.0 | 0.02 | 0.02 |
| 84.0 | 0.01 | 0.01 |
| 85.0 | 0.01 | 0.01 |
| 86.0 | 0.01 | 0.01 |
| 87.0 | 0.01 | 0.01 |
| 88.0 | 0.01 | 0.01 |
| 89.0 | 0.01 | 0 |
| 90.0 | 0 | 0 |
| 91.0 | 0 | 0 |
| 92.0 | 0.01 | 0 |
| 93.0 | 0 | 0 |
| 94.0 | 0 | 0 |
| 95.0 | 0 | 0 |
| 96.0 | 0 | 0 |
| 97.0 | 0 | 0 |
| 98.0 | 0 | 0 |
| 99.0 | 0 | 0 |
| 100.0 | 0 | 0 |
| 101.0 | 0 | 0 |
| 102.0 | 0 | 0 |
| 103.0 | 0 | 0 |
| 104.0 | 0 | 0 |
| 105.0 | 0 | 0 |
| 106.0 | 0 | 0 |
| 107.0 | 0 | 0 |
| 108.0 | 0 | 0 |
| 109.0 | 0 | 0 |
| 110.0 | 0 | 0 |
| 111.0 | 0 | 0 |
| 112.0 | 0 | 0 |
| 113.0 | 0 | 0 |
| 114.0 | 0 | 0 |
| 115.0 | 0 | 0 |
| 116.0 | 0 | 0 |
| 117.0 | 0 | 0 |
| 118.0 | 0 | 0 |
| 119.0 | 0 | 0 |
| 120.0 | 0 | 0 |
| 121.0 | 0 | 0 |
| 122.0 | 0 | 0 |
| 123.0 | 0 | 0 |
| 124.0 | 0 | 0 |
| 125.0 | 0 | 0 |
| 126.0 | 0 | 0 |
| 127.0 | 0 | 0 |
| 129.0 | 0 | 0 |
| 130.0 | 0 | 0 |
| 131.0 | 0 | 0 |
| 132.0 | 0 | 0 |
| 133.0 | 0 | 0 |
| 134.0 | 0 | 0 |
| 135.0 | 0 | 0 |
| 136.0 | 0 | 0 |
| 137.0 | 0 | 0 |
| 139.0 | 0 | 0 |
| 140.0 | 0 | 0 |
| 141.0 | 0 | 0 |
| 142.0 | 0 | 0 |
| 143.0 | 0 | 0 |
| 146.0 | 0 | 0 |
| 148.0 | 0 | 0 |
| 149.0 | 0 | 0 |
| 151.0 | 0 | 0 |
| 152.0 | 0 | 0 |
| 154.0 | 0 | 0 |
| 156.0 | 0 | 0 |
| 157.0 | 0 | 0 |
| 158.0 | 0 | 0 |
| 159.0 | 0 | 0 |
| 160.0 | 0 | 0 |
| 161.0 | 0 | 0 |
| 170.0 | 0 | 0 |
| 171.0 | 0 | 0 |
| 176.0 | 0 | 0 |
| 178.0 | 0 | 0 |
| 180.0 | 0 | 0 |
| 188.0 | 0 | 0 |
| 192.0 | 0 | 0 |
| 202.0 | 0 | 0 |
| 226.0 | 0 | 0 |
Months since last record¶
El número de meses desde el último registro público. Las observaciones ausentes significan que los individuos no han tenido episodios de morosidad, es así que los replazamos por valor 0.
In [111]:
loan['mths_since_last_record'].isnull().sum()
Out[111]:
1064777
In [112]:
loan['mths_since_last_record'].fillna(0, inplace = True)
In [113]:
plot_var('mths_since_last_record', 'Months since last record',
continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
In [114]:
purp_loan= ['mths_since_last_record', 'credit_risk']
cm = sns.light_palette("purple", as_cmap=True)
(round(pd.crosstab(loan[purp_loan[0]], loan[purp_loan[1]],
normalize='columns') * 100,2)).style.background_gradient(cmap = cm)
Out[114]:
| credit_risk | Default | No Default |
|---|---|---|
| mths_since_last_record | ||
| 0.0 | 80.71 | 83.68 |
| 1.0 | 0.01 | 0.01 |
| 2.0 | 0.01 | 0.01 |
| 3.0 | 0.02 | 0.01 |
| 4.0 | 0.02 | 0.02 |
| 5.0 | 0.03 | 0.02 |
| 6.0 | 0.03 | 0.02 |
| 7.0 | 0.03 | 0.03 |
| 8.0 | 0.03 | 0.03 |
| 9.0 | 0.04 | 0.03 |
| 10.0 | 0.05 | 0.03 |
| 11.0 | 0.05 | 0.03 |
| 12.0 | 0.05 | 0.04 |
| 13.0 | 0.04 | 0.03 |
| 14.0 | 0.05 | 0.04 |
| 15.0 | 0.04 | 0.04 |
| 16.0 | 0.05 | 0.04 |
| 17.0 | 0.04 | 0.04 |
| 18.0 | 0.06 | 0.05 |
| 19.0 | 0.05 | 0.05 |
| 20.0 | 0.05 | 0.05 |
| 21.0 | 0.05 | 0.05 |
| 22.0 | 0.06 | 0.05 |
| 23.0 | 0.06 | 0.06 |
| 24.0 | 0.07 | 0.06 |
| 25.0 | 0.08 | 0.06 |
| 26.0 | 0.08 | 0.06 |
| 27.0 | 0.08 | 0.07 |
| 28.0 | 0.08 | 0.07 |
| 29.0 | 0.09 | 0.07 |
| 30.0 | 0.08 | 0.08 |
| 31.0 | 0.09 | 0.08 |
| 32.0 | 0.1 | 0.08 |
| 33.0 | 0.11 | 0.09 |
| 34.0 | 0.1 | 0.1 |
| 35.0 | 0.1 | 0.09 |
| 36.0 | 0.11 | 0.1 |
| 37.0 | 0.13 | 0.11 |
| 38.0 | 0.12 | 0.12 |
| 39.0 | 0.14 | 0.11 |
| 40.0 | 0.13 | 0.12 |
| 41.0 | 0.14 | 0.12 |
| 42.0 | 0.15 | 0.13 |
| 43.0 | 0.15 | 0.13 |
| 44.0 | 0.14 | 0.14 |
| 45.0 | 0.16 | 0.14 |
| 46.0 | 0.17 | 0.15 |
| 47.0 | 0.16 | 0.16 |
| 48.0 | 0.17 | 0.16 |
| 49.0 | 0.17 | 0.16 |
| 50.0 | 0.17 | 0.16 |
| 51.0 | 0.18 | 0.17 |
| 52.0 | 0.2 | 0.18 |
| 53.0 | 0.2 | 0.19 |
| 54.0 | 0.22 | 0.2 |
| 55.0 | 0.23 | 0.2 |
| 56.0 | 0.23 | 0.2 |
| 57.0 | 0.23 | 0.21 |
| 58.0 | 0.25 | 0.21 |
| 59.0 | 0.24 | 0.2 |
| 60.0 | 0.25 | 0.21 |
| 61.0 | 0.25 | 0.22 |
| 62.0 | 0.25 | 0.23 |
| 63.0 | 0.27 | 0.23 |
| 64.0 | 0.26 | 0.23 |
| 65.0 | 0.25 | 0.22 |
| 66.0 | 0.27 | 0.23 |
| 67.0 | 0.27 | 0.24 |
| 68.0 | 0.27 | 0.23 |
| 69.0 | 0.29 | 0.24 |
| 70.0 | 0.29 | 0.24 |
| 71.0 | 0.31 | 0.24 |
| 72.0 | 0.28 | 0.24 |
| 73.0 | 0.27 | 0.24 |
| 74.0 | 0.3 | 0.23 |
| 75.0 | 0.29 | 0.24 |
| 76.0 | 0.29 | 0.24 |
| 77.0 | 0.28 | 0.24 |
| 78.0 | 0.3 | 0.23 |
| 79.0 | 0.29 | 0.24 |
| 80.0 | 0.29 | 0.24 |
| 81.0 | 0.3 | 0.23 |
| 82.0 | 0.31 | 0.22 |
| 83.0 | 0.27 | 0.21 |
| 84.0 | 0.23 | 0.18 |
| 85.0 | 0.24 | 0.19 |
| 86.0 | 0.26 | 0.19 |
| 87.0 | 0.26 | 0.18 |
| 88.0 | 0.21 | 0.18 |
| 89.0 | 0.22 | 0.16 |
| 90.0 | 0.21 | 0.16 |
| 91.0 | 0.22 | 0.15 |
| 92.0 | 0.21 | 0.16 |
| 93.0 | 0.18 | 0.16 |
| 94.0 | 0.19 | 0.15 |
| 95.0 | 0.19 | 0.15 |
| 96.0 | 0.2 | 0.14 |
| 97.0 | 0.18 | 0.15 |
| 98.0 | 0.17 | 0.15 |
| 99.0 | 0.16 | 0.14 |
| 100.0 | 0.19 | 0.14 |
| 101.0 | 0.17 | 0.14 |
| 102.0 | 0.18 | 0.14 |
| 103.0 | 0.18 | 0.14 |
| 104.0 | 0.16 | 0.14 |
| 105.0 | 0.17 | 0.14 |
| 106.0 | 0.15 | 0.13 |
| 107.0 | 0.16 | 0.13 |
| 108.0 | 0.16 | 0.14 |
| 109.0 | 0.15 | 0.14 |
| 110.0 | 0.13 | 0.13 |
| 111.0 | 0.15 | 0.13 |
| 112.0 | 0.15 | 0.14 |
| 113.0 | 0.15 | 0.14 |
| 114.0 | 0.16 | 0.14 |
| 115.0 | 0.15 | 0.14 |
| 116.0 | 0.15 | 0.14 |
| 117.0 | 0.14 | 0.14 |
| 118.0 | 0.14 | 0.14 |
| 119.0 | 0.08 | 0.07 |
| 120.0 | 0 | 0 |
| 121.0 | 0 | 0 |
| 123.0 | 0 | 0 |
| 129.0 | 0 | 0 |
Months since last major derogative¶
Meses desde la calificación más reciente de 90 días o peor. Las observaciones ausentes significan que los individuos no han tenido episodios de morosidad, es así que los replazamos por valor 0.
In [115]:
loan['mths_since_last_major_derog'].isnull().sum()
Out[115]:
946333
In [116]:
loan['mths_since_last_major_derog'].fillna(0, inplace = True)
In [117]:
plot_var('mths_since_last_major_derog', 'Months since last major derogative',
continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
In [118]:
purp_loan= ['mths_since_last_major_derog', 'credit_risk']
cm = sns.light_palette("purple", as_cmap=True)
(round(pd.crosstab(loan[purp_loan[0]], loan[purp_loan[1]],
normalize='columns') * 100,2)).style.background_gradient(cmap = cm)
Out[118]:
| credit_risk | Default | No Default |
|---|---|---|
| mths_since_last_major_derog | ||
| 0.0 | 71.18 | 74.42 |
| 1.0 | 0.09 | 0.07 |
| 2.0 | 0.1 | 0.08 |
| 3.0 | 0.1 | 0.09 |
| 4.0 | 0.15 | 0.11 |
| 5.0 | 0.17 | 0.15 |
| 6.0 | 0.22 | 0.18 |
| 7.0 | 0.24 | 0.19 |
| 8.0 | 0.25 | 0.2 |
| 9.0 | 0.27 | 0.21 |
| 10.0 | 0.26 | 0.22 |
| 11.0 | 0.26 | 0.21 |
| 12.0 | 0.32 | 0.25 |
| 13.0 | 0.31 | 0.28 |
| 14.0 | 0.33 | 0.27 |
| 15.0 | 0.32 | 0.28 |
| 16.0 | 0.31 | 0.29 |
| 17.0 | 0.33 | 0.29 |
| 18.0 | 0.35 | 0.3 |
| 19.0 | 0.32 | 0.29 |
| 20.0 | 0.34 | 0.31 |
| 21.0 | 0.36 | 0.32 |
| 22.0 | 0.38 | 0.32 |
| 23.0 | 0.37 | 0.32 |
| 24.0 | 0.38 | 0.35 |
| 25.0 | 0.4 | 0.36 |
| 26.0 | 0.42 | 0.37 |
| 27.0 | 0.39 | 0.36 |
| 28.0 | 0.42 | 0.37 |
| 29.0 | 0.4 | 0.37 |
| 30.0 | 0.41 | 0.36 |
| 31.0 | 0.42 | 0.37 |
| 32.0 | 0.41 | 0.37 |
| 33.0 | 0.4 | 0.37 |
| 34.0 | 0.41 | 0.37 |
| 35.0 | 0.42 | 0.37 |
| 36.0 | 0.45 | 0.37 |
| 37.0 | 0.45 | 0.39 |
| 38.0 | 0.42 | 0.4 |
| 39.0 | 0.43 | 0.38 |
| 40.0 | 0.44 | 0.4 |
| 41.0 | 0.42 | 0.4 |
| 42.0 | 0.45 | 0.4 |
| 43.0 | 0.44 | 0.4 |
| 44.0 | 0.42 | 0.4 |
| 45.0 | 0.44 | 0.41 |
| 46.0 | 0.43 | 0.4 |
| 47.0 | 0.42 | 0.39 |
| 48.0 | 0.44 | 0.39 |
| 49.0 | 0.43 | 0.37 |
| 50.0 | 0.37 | 0.35 |
| 51.0 | 0.4 | 0.35 |
| 52.0 | 0.38 | 0.36 |
| 53.0 | 0.38 | 0.36 |
| 54.0 | 0.41 | 0.35 |
| 55.0 | 0.38 | 0.35 |
| 56.0 | 0.39 | 0.36 |
| 57.0 | 0.38 | 0.35 |
| 58.0 | 0.38 | 0.35 |
| 59.0 | 0.39 | 0.35 |
| 60.0 | 0.38 | 0.35 |
| 61.0 | 0.39 | 0.35 |
| 62.0 | 0.4 | 0.35 |
| 63.0 | 0.4 | 0.34 |
| 64.0 | 0.39 | 0.35 |
| 65.0 | 0.38 | 0.35 |
| 66.0 | 0.38 | 0.35 |
| 67.0 | 0.38 | 0.35 |
| 68.0 | 0.36 | 0.35 |
| 69.0 | 0.39 | 0.34 |
| 70.0 | 0.39 | 0.34 |
| 71.0 | 0.37 | 0.32 |
| 72.0 | 0.35 | 0.32 |
| 73.0 | 0.34 | 0.32 |
| 74.0 | 0.36 | 0.3 |
| 75.0 | 0.33 | 0.29 |
| 76.0 | 0.32 | 0.28 |
| 77.0 | 0.26 | 0.24 |
| 78.0 | 0.24 | 0.22 |
| 79.0 | 0.25 | 0.2 |
| 80.0 | 0.21 | 0.18 |
| 81.0 | 0.19 | 0.16 |
| 82.0 | 0.1 | 0.08 |
| 83.0 | 0.03 | 0.03 |
| 84.0 | 0.02 | 0.02 |
| 85.0 | 0.02 | 0.02 |
| 86.0 | 0.02 | 0.01 |
| 87.0 | 0.01 | 0.01 |
| 88.0 | 0.01 | 0.01 |
| 89.0 | 0.01 | 0.01 |
| 90.0 | 0.01 | 0.01 |
| 91.0 | 0.01 | 0.01 |
| 92.0 | 0.01 | 0.01 |
| 93.0 | 0.01 | 0.01 |
| 94.0 | 0.01 | 0.01 |
| 95.0 | 0.01 | 0.01 |
| 96.0 | 0.01 | 0 |
| 97.0 | 0.01 | 0 |
| 98.0 | 0.01 | 0.01 |
| 99.0 | 0 | 0 |
| 100.0 | 0 | 0 |
| 101.0 | 0 | 0 |
| 102.0 | 0 | 0 |
| 103.0 | 0 | 0 |
| 104.0 | 0 | 0 |
| 105.0 | 0 | 0 |
| 106.0 | 0 | 0 |
| 107.0 | 0 | 0 |
| 108.0 | 0 | 0 |
| 109.0 | 0 | 0 |
| 110.0 | 0 | 0 |
| 111.0 | 0 | 0 |
| 112.0 | 0 | 0 |
| 113.0 | 0 | 0 |
| 114.0 | 0 | 0 |
| 115.0 | 0 | 0 |
| 116.0 | 0 | 0 |
| 117.0 | 0 | 0 |
| 118.0 | 0 | 0 |
| 119.0 | 0 | 0 |
| 120.0 | 0 | 0 |
| 121.0 | 0 | 0 |
| 122.0 | 0 | 0 |
| 123.0 | 0 | 0 |
| 124.0 | 0 | 0 |
| 125.0 | 0 | 0 |
| 126.0 | 0 | 0 |
| 127.0 | 0 | 0 |
| 129.0 | 0 | 0 |
| 130.0 | 0 | 0 |
| 131.0 | 0 | 0 |
| 132.0 | 0 | 0 |
| 133.0 | 0 | 0 |
| 134.0 | 0 | 0 |
| 135.0 | 0 | 0 |
| 136.0 | 0 | 0 |
| 137.0 | 0 | 0 |
| 138.0 | 0 | 0 |
| 139.0 | 0 | 0 |
| 140.0 | 0 | 0 |
| 141.0 | 0 | 0 |
| 142.0 | 0 | 0 |
| 143.0 | 0 | 0 |
| 144.0 | 0 | 0 |
| 145.0 | 0 | 0 |
| 146.0 | 0 | 0 |
| 147.0 | 0 | 0 |
| 148.0 | 0 | 0 |
| 149.0 | 0 | 0 |
| 150.0 | 0 | 0 |
| 151.0 | 0 | 0 |
| 152.0 | 0 | 0 |
| 153.0 | 0 | 0 |
| 154.0 | 0 | 0 |
| 156.0 | 0 | 0 |
| 157.0 | 0 | 0 |
| 158.0 | 0 | 0 |
| 159.0 | 0 | 0 |
| 160.0 | 0 | 0 |
| 161.0 | 0 | 0 |
| 162.0 | 0 | 0 |
| 165.0 | 0 | 0 |
| 170.0 | 0 | 0 |
| 171.0 | 0 | 0 |
| 175.0 | 0 | 0 |
| 176.0 | 0 | 0 |
| 178.0 | 0 | 0 |
| 180.0 | 0 | 0 |
| 188.0 | 0 | 0 |
| 192.0 | 0 | 0 |
| 197.0 | 0 | 0 |
| 202.0 | 0 | 0 |
| 226.0 | 0 | 0 |
Collections 12 Months (Exclude Medical Credits)¶
Número de cobros en 12 meses excluyendo cobros médicos.Las Observaciones ausentes se van a imputar a 0, dado que representan los que no han tenido cobros en el último año.
In [119]:
loan['collections_12_mths_ex_med'].isnull().sum()
Out[119]:
145
In [120]:
loan['collections_12_mths_ex_med'].fillna(0, inplace = True)
In [121]:
plot_var('collections_12_mths_ex_med', 'Collections 12 Months',
continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Total collection amount¶
Montos totales de cobro adeudados. Las observaciones ausentes se imputaran a 0 dado que representan los casos que no tienen registros de cobros.
In [122]:
loan['tot_coll_amt'].isnull().sum()
Out[122]:
70276
In [123]:
loan['tot_coll_amt'].fillna(0, inplace = True)
In [124]:
plot_var('tot_coll_amt', 'Total collection amounts ever owed',
continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Accounts open past 24 months¶
Número de operaciones abiertas en los últimos 24 meses. Las observaciones ausentes representan los solicitantes que no han abierto cuentas durante los últimos 24 meses.
In [125]:
loan['acc_open_past_24mths'].isnull().sum()
Out[125]:
50030
In [126]:
loan['acc_open_past_24mths'].fillna(0, inplace = True)
In [127]:
plot_var('acc_open_past_24mths', 'Number of trades opened in past 24 months',
continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Chargeoff within 12 months¶
Número de eventos impagos dentro de los 12 meses. Las observaciones ausentes se imputaran a 0 dado que no han reportado eventos de cuentas impagables.
In [128]:
loan['chargeoff_within_12_mths'].isnull().sum()
Out[128]:
145
In [129]:
loan['chargeoff_within_12_mths'].fillna(0, inplace = True)
In [130]:
plot_var('chargeoff_within_12_mths', 'Number of charge-offs within 12 months',
continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Months since old Credit Account¶
Meses desde que se abrió la cuenta bancaria más antigua.
In [131]:
loan['mo_sin_old_il_acct'].isnull().sum()
Out[131]:
106370
In [132]:
loan['mo_sin_old_il_acct'].fillna(0, inplace = True)
In [133]:
plot_var('mo_sin_old_il_acct', 'Months since oldest bank installment account opened',
continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Months since old opened revolvent account¶
Meses desde que se abrió la cuenta rotativa más antigua.
In [134]:
loan['mo_sin_old_rev_tl_op'].isnull().sum()
Out[134]:
70277
In [135]:
loan['mo_sin_old_rev_tl_op'].fillna(0, inplace = True)
In [136]:
plot_var('mo_sin_old_rev_tl_op', 'Months since oldest revolving account opened',
continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Months since recent opened account¶
Meses desde que se abrió la cuenta más reciente. Se imputaran las observaciones ausentes.
In [137]:
loan['mo_sin_rcnt_tl'].isnull().sum()
Out[137]:
70276
In [138]:
loan['mo_sin_rcnt_tl'].fillna(0, inplace = True)
In [139]:
plot_var('mo_sin_rcnt_tl', 'Months since most recent account opened',
continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Number of accounts ever 120 or more days past due¶
In [140]:
loan['num_accts_ever_120_pd'].isnull().sum()
Out[140]:
70276
In [141]:
loan['num_accts_ever_120_pd'].fillna(0, inplace = True)
In [142]:
plot_var('num_accts_ever_120_pd', 'Number of accounts ever 120 or more days past due', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Mantenemos la variable por considerar que tiene valor discriminante.
Number of installment accounts¶
In [143]:
loan['num_il_tl'].isnull().sum()
Out[143]:
70276
In [144]:
loan['num_il_tl'].fillna(0, inplace = True)
In [145]:
plot_var('num_il_tl', 'Number of installment accounts', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Eliminamos la variable por considerar que no posee poder discriminante.
In [146]:
loan.drop(['num_il_tl'], axis = 1, inplace = True)
Number of accounts currently 120 days past due (updated in past 2 months)¶
In [147]:
loan['num_tl_120dpd_2m'].isnull().sum()
Out[147]:
118319
In [148]:
loan['num_tl_120dpd_2m'].fillna(0, inplace = True)
In [149]:
plot_var('num_tl_120dpd_2m', 'Number of accounts currently 120 days past due', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Prescindimos de la variable por no tener poder predictivo.
In [150]:
loan.drop(['num_tl_120dpd_2m'], axis = 1, inplace = True)
Number of accounts currently 30 days past due (updated in past 2 months)¶
In [151]:
loan['num_tl_30dpd'].isnull().sum()
Out[151]:
70276
In [152]:
loan['num_tl_30dpd'].fillna(0, inplace = True)
In [153]:
plot_var('num_tl_30dpd', 'Number of accounts currently 30 days past due', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Eliminamos la variable.
In [154]:
loan.drop(['num_tl_30dpd'], axis = 1, inplace = True)
Number of accounts 90 or more days past due in last 24 months¶
In [155]:
loan['num_tl_90g_dpd_24m'].isnull().sum()
Out[155]:
70276
In [156]:
loan['num_tl_90g_dpd_24m'].fillna(0, inplace = True)
In [157]:
plot_var('num_tl_90g_dpd_24m', 'Number of accounts 90 or more days past due in last 24 months', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Number of accounts opened in past 12 months¶
In [158]:
loan['num_tl_op_past_12m'].isnull().sum()
Out[158]:
70276
In [159]:
loan['num_tl_op_past_12m'].fillna(0, inplace = True)
In [160]:
plot_var('num_tl_op_past_12m', 'Number of accounts opened in past 12 months', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Eliminamos la variable, ya que los préstamos default y no default presentan comportamientos similares en esta variable y no permite predecir.
In [161]:
loan.drop(['num_tl_op_past_12m'], axis = 1, inplace = True)
Percent of trades never delinquent¶
In [162]:
loan['pct_tl_nvr_dlq'].isna().sum()
Out[162]:
70430
In [163]:
loan['pct_tl_nvr_dlq'].fillna(0, inplace = True)
In [164]:
plot_var('pct_tl_nvr_dlq', 'Percent of trades never delinquent', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Prescindimos de esta variable.
In [165]:
loan.drop(['pct_tl_nvr_dlq'], axis = 1, inplace = True)
Percentage of all bankcard accounts > 75% of limit.¶
In [166]:
loan['percent_bc_gt_75'].isna().sum()
Out[166]:
63264
In [167]:
loan['percent_bc_gt_75'].fillna(0, inplace = True)
In [168]:
plot_var('percent_bc_gt_75', 'Percentage of all bankcard accounts > 75% of limit', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Number of public record bankruptcies¶
In [169]:
loan['pub_rec_bankruptcies'].isna().sum()
Out[169]:
1365
In [170]:
loan['pub_rec_bankruptcies'].fillna(0, inplace = True)
In [171]:
plot_var('pub_rec_bankruptcies', 'Number of public record bankruptcies', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Esta variable no posee poder discriminante, por lo que la eliminamos del estudio.
In [172]:
loan.drop(['pub_rec_bankruptcies'], axis = 1, inplace = True)
In [173]:
loan.drop(['open_acc_6m','open_act_il','open_il_12m',
'open_il_24m','mths_since_rcnt_il','open_rv_12m',
'open_rv_24m','inq_fi','inq_last_12m','mo_sin_rcnt_rev_tl_op'
], axis = 1, inplace = True)
loan.shape
Out[173]:
(1283143, 96)
Endeudamiento del Prestatario¶
Esta sección esta representada por el Ratio DTI.
Total monthly debt payments on the total debt obligations (DTI)¶
In [174]:
loan['dti'].describe()
Out[174]:
count 1.283143e+06 mean 1.800362e+01 std 8.359684e+00 min -1.000000e+00 25% 1.174000e+01 50% 1.752000e+01 75% 2.388000e+01 max 4.996000e+01 Name: dti, dtype: float64
In [175]:
f, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12,3), dpi=90)
sns.distplot(loan.loc[loan['dti'].notnull() & (loan['dti'] < 60), 'dti'], kde=False, ax=ax1)
ax1.set_xlabel('dti')
ax1.set_ylabel('Count')
ax1.set_title('Deuda a Ingresos')
sns.boxplot(x=loan.loc[loan['dti'].notnull() & (loan['dti'] < 60), 'dti'], y='credit_risk', data=loan, ax=ax2)
ax2.set_xlabel('DTI')
ax2.set_ylabel('Proporcion de préstamos pagados')
ax2.set_title('Tasa totalmente pagada a ingresos')
ax2.set_title('DTI por Default')
Out[175]:
Text(0.5, 1.0, 'DTI por Default')
In [176]:
(loan['dti'] > 40).sum() / (loan['dti']).sum()
Out[176]:
7.220397493054618e-05
In [177]:
loan.groupby('credit_risk')['dti'].median()
Out[177]:
credit_risk Default 19.66 No Default 17.01 Name: dti, dtype: float64
Caracteristicas Financieras Actuales¶
Bajo este epígrafe agruparemos todas las variables que describen el historial crediticio del usuario.
- 'open_acc' : The number of open credit lines in the borrower's credit file.
- 'revol_bal' : Total credit revolving balance.
- 'revol_util' : Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit.
- 'total_acc' : The total number of credit lines currently in the borrower's credit file.
- 'acc_now_delinq' : The number of accounts on which the borrower is now delinquent.
- 'tot_cur_bal' : Total current balance of all accounts.
- 'total_bal_il' : Total current balance of all installment accounts.
- 'il_util' : Ratio of total current balance to high credit/credit limit on all install acct.
- 'max_bal_bc' : Maximum current balance owed on all revolving accounts.
- 'all_util' : Balance to credit limit on all trades.
- 'total_rev_hi_lim' : Total revolving high credit/credit limit.
- 'total_cu_tl' : Number of finance trades.
- 'avg_cur_bal' : Average current balance of all accounts.
- 'bc_open_to_buy' : Total open to buy on revolving bankcards.
- 'bc_util' : Ratio of total current balance to high credit/credit limit for all bankcard accounts.
- 'delinq_amnt' : The past-due amount owed for the accounts on which the borrower is now delinquent.
- 'mort_acc' : Number of mortgage accounts.
Open Accounts (Open Acc)¶
'Open_acc' es el número de líneas de crédito abiertas en el archivo de crédito del prestatario. 'total_acc' es el número total de líneas de crédito actualmente en el archivo de crédito del prestatario. La proporción significa cuántos préstamos tiene el solicitante ahora.
In [178]:
loan['open_acc'].fillna(0, inplace = True)
In [179]:
loan['acc_ratio'] = loan.open_acc / loan.total_acc
In [180]:
loan['acc_ratio'].fillna(0, inplace = True)
In [181]:
plot_var('acc_ratio', 'Ratio Open Accounts', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Revolving Balance (Revol Acc)¶
In [182]:
loan['revol_bal'].isna().sum()
Out[182]:
0
In [183]:
plot_var('revol_bal', 'Revolving Balance', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
In [184]:
loan["revol_bal_cat"] = 0
loan.loc[loan["revol_bal"]>0,"revol_bal_cat"] = 1
In [185]:
purp_loan= ['revol_bal_cat', 'credit_risk']
cm = sns.light_palette("purple", as_cmap=True)
(round(pd.crosstab(loan[purp_loan[0]], loan[purp_loan[1]],
normalize='columns') * 100,2)).style.background_gradient(cmap = cm)
Out[185]:
| credit_risk | Default | No Default |
|---|---|---|
| revol_bal_cat | ||
| 0 | 0.42 | 0.49 |
| 1 | 99.58 | 99.51 |
In [186]:
loan.drop(['revol_bal'],
axis = 1, inplace = True)
Revolving line utilization (Revol Util)¶
In [187]:
loan['revol_util'].isnull().sum()
loan['revol_util'].fillna(0, inplace = True)
plot_var('revol_util', 'Revolving line utilization', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
In [188]:
purp_loan= ['revol_util', 'credit_risk']
cm = sns.light_palette("purple", as_cmap=True)
(round(pd.crosstab(loan[purp_loan[0]], loan[purp_loan[1]],
normalize='columns') * 100,2)).style.background_gradient(cmap = cm)
Out[188]:
| credit_risk | Default | No Default |
|---|---|---|
| revol_util | ||
| 0.0 | 0.48 | 0.59 |
| 0.01 | 0 | 0 |
| 0.03 | 0 | 0 |
| 0.04 | 0 | 0 |
| 0.05 | 0 | 0 |
| 0.1 | 0.06 | 0.07 |
| 0.12 | 0 | 0 |
| 0.16 | 0 | 0 |
| 0.2 | 0.05 | 0.05 |
| 0.3 | 0.05 | 0.05 |
| 0.4 | 0.04 | 0.05 |
| 0.46 | 0 | 0 |
| 0.49 | 0 | 0 |
| 0.5 | 0.04 | 0.04 |
| 0.54 | 0 | 0 |
| 0.6 | 0.03 | 0.04 |
| 0.7 | 0.03 | 0.04 |
| 0.75 | 0 | 0 |
| 0.8 | 0.03 | 0.04 |
| 0.83 | 0 | 0 |
| 0.86 | 0 | 0 |
| 0.9 | 0.02 | 0.04 |
| 1.0 | 0.03 | 0.04 |
| 1.1 | 0.03 | 0.04 |
| 1.2 | 0.03 | 0.04 |
| 1.3 | 0.03 | 0.04 |
| 1.4 | 0.03 | 0.04 |
| 1.5 | 0.03 | 0.04 |
| 1.6 | 0.03 | 0.04 |
| 1.7 | 0.03 | 0.04 |
| 1.8 | 0.03 | 0.04 |
| 1.88 | 0 | 0 |
| 1.9 | 0.02 | 0.04 |
| 2.0 | 0.03 | 0.04 |
| 2.1 | 0.03 | 0.04 |
| 2.2 | 0.03 | 0.04 |
| 2.3 | 0.03 | 0.04 |
| 2.4 | 0.03 | 0.04 |
| 2.5 | 0.03 | 0.04 |
| 2.6 | 0.02 | 0.04 |
| 2.64 | 0 | 0 |
| 2.7 | 0.03 | 0.04 |
| 2.8 | 0.02 | 0.04 |
| 2.9 | 0.02 | 0.04 |
| 3.0 | 0.04 | 0.04 |
| 3.1 | 0.02 | 0.04 |
| 3.18 | 0 | 0 |
| 3.2 | 0.03 | 0.04 |
| 3.3 | 0.02 | 0.04 |
| 3.4 | 0.03 | 0.04 |
| 3.5 | 0.02 | 0.04 |
| 3.6 | 0.02 | 0.04 |
| 3.7 | 0.02 | 0.04 |
| 3.8 | 0.02 | 0.04 |
| 3.9 | 0.02 | 0.04 |
| 4.0 | 0.04 | 0.05 |
| 4.1 | 0.02 | 0.04 |
| 4.2 | 0.03 | 0.04 |
| 4.3 | 0.03 | 0.04 |
| 4.4 | 0.02 | 0.04 |
| 4.5 | 0.02 | 0.04 |
| 4.6 | 0.02 | 0.04 |
| 4.7 | 0.02 | 0.04 |
| 4.8 | 0.03 | 0.04 |
| 4.85 | 0 | 0 |
| 4.9 | 0.03 | 0.04 |
| 5.0 | 0.03 | 0.05 |
| 5.1 | 0.03 | 0.04 |
| 5.2 | 0.03 | 0.05 |
| 5.3 | 0.03 | 0.04 |
| 5.33 | 0 | 0 |
| 5.34 | 0 | 0 |
| 5.4 | 0.03 | 0.04 |
| 5.5 | 0.02 | 0.04 |
| 5.6 | 0.03 | 0.04 |
| 5.7 | 0.03 | 0.04 |
| 5.79 | 0 | 0 |
| 5.8 | 0.04 | 0.04 |
| 5.9 | 0.02 | 0.05 |
| 6.0 | 0.03 | 0.05 |
| 6.1 | 0.03 | 0.05 |
| 6.2 | 0.03 | 0.04 |
| 6.3 | 0.03 | 0.04 |
| 6.4 | 0.03 | 0.04 |
| 6.5 | 0.02 | 0.05 |
| 6.6 | 0.03 | 0.04 |
| 6.7 | 0.03 | 0.04 |
| 6.75 | 0 | 0 |
| 6.8 | 0.03 | 0.04 |
| 6.9 | 0.03 | 0.05 |
| 7.0 | 0.04 | 0.05 |
| 7.1 | 0.03 | 0.04 |
| 7.2 | 0.03 | 0.05 |
| 7.28 | 0 | 0 |
| 7.3 | 0.03 | 0.05 |
| 7.4 | 0.03 | 0.05 |
| 7.43 | 0 | 0 |
| 7.5 | 0.03 | 0.05 |
| 7.6 | 0.03 | 0.05 |
| 7.64 | 0 | 0 |
| 7.7 | 0.03 | 0.05 |
| 7.8 | 0.03 | 0.05 |
| 7.9 | 0.03 | 0.06 |
| 8.0 | 0.04 | 0.06 |
| 8.01 | 0 | 0 |
| 8.1 | 0.03 | 0.05 |
| 8.2 | 0.03 | 0.06 |
| 8.3 | 0.03 | 0.05 |
| 8.4 | 0.04 | 0.05 |
| 8.46 | 0 | 0 |
| 8.49 | 0 | 0 |
| 8.5 | 0.03 | 0.04 |
| 8.58 | 0 | 0 |
| 8.6 | 0.03 | 0.05 |
| 8.7 | 0.03 | 0.05 |
| 8.8 | 0.03 | 0.05 |
| 8.9 | 0.03 | 0.05 |
| 9.0 | 0.04 | 0.06 |
| 9.1 | 0.03 | 0.05 |
| 9.2 | 0.03 | 0.05 |
| 9.3 | 0.04 | 0.05 |
| 9.34 | 0 | 0 |
| 9.4 | 0.03 | 0.05 |
| 9.5 | 0.03 | 0.06 |
| 9.6 | 0.02 | 0.06 |
| 9.7 | 0.04 | 0.06 |
| 9.71 | 0 | 0 |
| 9.8 | 0.03 | 0.05 |
| 9.9 | 0.04 | 0.06 |
| 10.0 | 0.05 | 0.06 |
| 10.08 | 0 | 0 |
| 10.1 | 0.04 | 0.06 |
| 10.17 | 0 | 0 |
| 10.2 | 0.03 | 0.05 |
| 10.3 | 0.03 | 0.05 |
| 10.4 | 0.03 | 0.05 |
| 10.5 | 0.04 | 0.06 |
| 10.6 | 0.03 | 0.06 |
| 10.61 | 0 | 0 |
| 10.7 | 0.04 | 0.06 |
| 10.8 | 0.04 | 0.06 |
| 10.9 | 0.03 | 0.06 |
| 11.0 | 0.05 | 0.06 |
| 11.1 | 0.04 | 0.06 |
| 11.2 | 0.04 | 0.06 |
| 11.3 | 0.04 | 0.06 |
| 11.4 | 0.03 | 0.06 |
| 11.5 | 0.04 | 0.06 |
| 11.6 | 0.04 | 0.06 |
| 11.62 | 0 | 0 |
| 11.63 | 0 | 0 |
| 11.7 | 0.04 | 0.06 |
| 11.8 | 0.04 | 0.06 |
| 11.9 | 0.03 | 0.06 |
| 12.0 | 0.05 | 0.07 |
| 12.1 | 0.05 | 0.06 |
| 12.2 | 0.04 | 0.06 |
| 12.3 | 0.04 | 0.06 |
| 12.4 | 0.04 | 0.06 |
| 12.42 | 0 | 0 |
| 12.5 | 0.03 | 0.06 |
| 12.6 | 0.04 | 0.06 |
| 12.7 | 0.04 | 0.06 |
| 12.8 | 0.05 | 0.07 |
| 12.9 | 0.05 | 0.06 |
| 13.0 | 0.05 | 0.07 |
| 13.1 | 0.04 | 0.06 |
| 13.2 | 0.04 | 0.06 |
| 13.3 | 0.05 | 0.06 |
| 13.4 | 0.04 | 0.06 |
| 13.5 | 0.04 | 0.06 |
| 13.56 | 0 | 0 |
| 13.6 | 0.05 | 0.06 |
| 13.7 | 0.04 | 0.06 |
| 13.8 | 0.05 | 0.06 |
| 13.9 | 0.05 | 0.07 |
| 14.0 | 0.06 | 0.08 |
| 14.1 | 0.05 | 0.06 |
| 14.2 | 0.04 | 0.07 |
| 14.3 | 0.05 | 0.07 |
| 14.4 | 0.04 | 0.07 |
| 14.5 | 0.04 | 0.07 |
| 14.6 | 0.05 | 0.07 |
| 14.7 | 0.04 | 0.07 |
| 14.8 | 0.05 | 0.07 |
| 14.9 | 0.04 | 0.06 |
| 15.0 | 0.06 | 0.08 |
| 15.1 | 0.04 | 0.07 |
| 15.2 | 0.05 | 0.07 |
| 15.3 | 0.05 | 0.06 |
| 15.4 | 0.04 | 0.07 |
| 15.5 | 0.05 | 0.07 |
| 15.6 | 0.06 | 0.07 |
| 15.7 | 0.05 | 0.07 |
| 15.8 | 0.06 | 0.07 |
| 15.9 | 0.05 | 0.07 |
| 16.0 | 0.06 | 0.08 |
| 16.02 | 0 | 0 |
| 16.1 | 0.06 | 0.08 |
| 16.2 | 0.05 | 0.07 |
| 16.3 | 0.06 | 0.07 |
| 16.4 | 0.04 | 0.07 |
| 16.5 | 0.06 | 0.07 |
| 16.6 | 0.05 | 0.07 |
| 16.7 | 0.06 | 0.07 |
| 16.8 | 0.06 | 0.08 |
| 16.9 | 0.05 | 0.07 |
| 17.0 | 0.07 | 0.09 |
| 17.1 | 0.06 | 0.07 |
| 17.2 | 0.05 | 0.07 |
| 17.3 | 0.05 | 0.08 |
| 17.4 | 0.05 | 0.07 |
| 17.5 | 0.05 | 0.08 |
| 17.6 | 0.05 | 0.07 |
| 17.67 | 0 | 0 |
| 17.7 | 0.05 | 0.08 |
| 17.71 | 0 | 0 |
| 17.78 | 0 | 0 |
| 17.8 | 0.05 | 0.07 |
| 17.9 | 0.05 | 0.08 |
| 18.0 | 0.08 | 0.1 |
| 18.1 | 0.05 | 0.07 |
| 18.2 | 0.06 | 0.08 |
| 18.3 | 0.05 | 0.08 |
| 18.4 | 0.06 | 0.08 |
| 18.5 | 0.06 | 0.08 |
| 18.6 | 0.06 | 0.08 |
| 18.7 | 0.07 | 0.08 |
| 18.8 | 0.06 | 0.08 |
| 18.82 | 0 | 0 |
| 18.9 | 0.05 | 0.08 |
| 19.0 | 0.08 | 0.1 |
| 19.1 | 0.07 | 0.08 |
| 19.2 | 0.06 | 0.08 |
| 19.3 | 0.06 | 0.08 |
| 19.4 | 0.06 | 0.08 |
| 19.5 | 0.06 | 0.08 |
| 19.6 | 0.06 | 0.08 |
| 19.7 | 0.07 | 0.08 |
| 19.8 | 0.06 | 0.08 |
| 19.9 | 0.07 | 0.08 |
| 20.0 | 0.08 | 0.1 |
| 20.1 | 0.07 | 0.08 |
| 20.2 | 0.06 | 0.08 |
| 20.3 | 0.07 | 0.09 |
| 20.4 | 0.06 | 0.08 |
| 20.5 | 0.07 | 0.09 |
| 20.6 | 0.07 | 0.08 |
| 20.7 | 0.07 | 0.08 |
| 20.8 | 0.06 | 0.08 |
| 20.9 | 0.06 | 0.09 |
| 21.0 | 0.08 | 0.1 |
| 21.1 | 0.07 | 0.08 |
| 21.2 | 0.07 | 0.09 |
| 21.3 | 0.07 | 0.08 |
| 21.4 | 0.07 | 0.08 |
| 21.5 | 0.08 | 0.09 |
| 21.59 | 0 | 0 |
| 21.6 | 0.06 | 0.08 |
| 21.7 | 0.06 | 0.09 |
| 21.72 | 0 | 0 |
| 21.8 | 0.07 | 0.09 |
| 21.9 | 0.07 | 0.09 |
| 21.92 | 0 | 0 |
| 22.0 | 0.1 | 0.11 |
| 22.1 | 0.07 | 0.09 |
| 22.2 | 0.07 | 0.09 |
| 22.3 | 0.08 | 0.09 |
| 22.4 | 0.07 | 0.09 |
| 22.5 | 0.07 | 0.09 |
| 22.6 | 0.07 | 0.09 |
| 22.7 | 0.07 | 0.09 |
| 22.8 | 0.07 | 0.09 |
| 22.9 | 0.06 | 0.09 |
| 23.0 | 0.1 | 0.11 |
| 23.1 | 0.06 | 0.09 |
| 23.11 | 0 | 0 |
| 23.2 | 0.06 | 0.09 |
| 23.3 | 0.08 | 0.09 |
| 23.4 | 0.07 | 0.09 |
| 23.5 | 0.07 | 0.09 |
| 23.6 | 0.08 | 0.08 |
| 23.7 | 0.08 | 0.09 |
| 23.8 | 0.07 | 0.09 |
| 23.9 | 0.08 | 0.1 |
| 24.0 | 0.09 | 0.12 |
| 24.1 | 0.08 | 0.1 |
| 24.2 | 0.08 | 0.09 |
| 24.3 | 0.08 | 0.1 |
| 24.4 | 0.07 | 0.1 |
| 24.5 | 0.08 | 0.09 |
| 24.6 | 0.07 | 0.1 |
| 24.63 | 0 | 0 |
| 24.65 | 0 | 0 |
| 24.66 | 0 | 0 |
| 24.7 | 0.08 | 0.1 |
| 24.8 | 0.08 | 0.09 |
| 24.89 | 0 | 0 |
| 24.9 | 0.09 | 0.1 |
| 25.0 | 0.09 | 0.13 |
| 25.1 | 0.08 | 0.1 |
| 25.2 | 0.08 | 0.1 |
| 25.3 | 0.07 | 0.1 |
| 25.33 | 0 | 0 |
| 25.4 | 0.07 | 0.1 |
| 25.5 | 0.08 | 0.1 |
| 25.6 | 0.08 | 0.09 |
| 25.7 | 0.08 | 0.1 |
| 25.74 | 0 | 0 |
| 25.8 | 0.07 | 0.1 |
| 25.9 | 0.08 | 0.09 |
| 26.0 | 0.12 | 0.13 |
| 26.1 | 0.07 | 0.1 |
| 26.2 | 0.08 | 0.1 |
| 26.3 | 0.09 | 0.1 |
| 26.32 | 0 | 0 |
| 26.33 | 0 | 0 |
| 26.4 | 0.07 | 0.1 |
| 26.5 | 0.09 | 0.1 |
| 26.6 | 0.08 | 0.11 |
| 26.7 | 0.09 | 0.1 |
| 26.8 | 0.09 | 0.1 |
| 26.9 | 0.08 | 0.1 |
| 27.0 | 0.12 | 0.13 |
| 27.1 | 0.08 | 0.11 |
| 27.2 | 0.08 | 0.1 |
| 27.3 | 0.09 | 0.1 |
| 27.4 | 0.08 | 0.1 |
| 27.5 | 0.09 | 0.11 |
| 27.6 | 0.09 | 0.11 |
| 27.7 | 0.09 | 0.1 |
| 27.8 | 0.09 | 0.1 |
| 27.81 | 0 | 0 |
| 27.9 | 0.09 | 0.1 |
| 28.0 | 0.14 | 0.13 |
| 28.1 | 0.08 | 0.11 |
| 28.2 | 0.08 | 0.1 |
| 28.3 | 0.09 | 0.11 |
| 28.4 | 0.1 | 0.11 |
| 28.41 | 0 | 0 |
| 28.5 | 0.09 | 0.1 |
| 28.6 | 0.1 | 0.1 |
| 28.7 | 0.09 | 0.11 |
| 28.8 | 0.09 | 0.11 |
| 28.9 | 0.08 | 0.1 |
| 29.0 | 0.12 | 0.14 |
| 29.1 | 0.09 | 0.11 |
| 29.2 | 0.09 | 0.11 |
| 29.3 | 0.09 | 0.11 |
| 29.4 | 0.09 | 0.12 |
| 29.5 | 0.09 | 0.11 |
| 29.53 | 0 | 0 |
| 29.6 | 0.1 | 0.11 |
| 29.7 | 0.09 | 0.11 |
| 29.77 | 0 | 0 |
| 29.8 | 0.1 | 0.11 |
| 29.9 | 0.1 | 0.12 |
| 30.0 | 0.14 | 0.14 |
| 30.1 | 0.09 | 0.11 |
| 30.19 | 0 | 0 |
| 30.2 | 0.09 | 0.11 |
| 30.3 | 0.09 | 0.11 |
| 30.4 | 0.1 | 0.11 |
| 30.5 | 0.1 | 0.12 |
| 30.6 | 0.09 | 0.11 |
| 30.7 | 0.09 | 0.12 |
| 30.8 | 0.1 | 0.11 |
| 30.9 | 0.09 | 0.11 |
| 31.0 | 0.14 | 0.15 |
| 31.1 | 0.11 | 0.11 |
| 31.2 | 0.1 | 0.12 |
| 31.3 | 0.09 | 0.12 |
| 31.4 | 0.09 | 0.11 |
| 31.5 | 0.1 | 0.12 |
| 31.6 | 0.1 | 0.12 |
| 31.7 | 0.11 | 0.11 |
| 31.8 | 0.09 | 0.11 |
| 31.9 | 0.1 | 0.12 |
| 32.0 | 0.15 | 0.15 |
| 32.04 | 0 | 0 |
| 32.1 | 0.1 | 0.11 |
| 32.2 | 0.1 | 0.11 |
| 32.3 | 0.1 | 0.12 |
| 32.4 | 0.1 | 0.12 |
| 32.5 | 0.1 | 0.11 |
| 32.6 | 0.11 | 0.12 |
| 32.7 | 0.11 | 0.12 |
| 32.71 | 0 | 0 |
| 32.8 | 0.1 | 0.11 |
| 32.9 | 0.1 | 0.11 |
| 33.0 | 0.16 | 0.16 |
| 33.1 | 0.11 | 0.12 |
| 33.14 | 0 | 0 |
| 33.2 | 0.11 | 0.11 |
| 33.26 | 0 | 0 |
| 33.29 | 0 | 0 |
| 33.3 | 0.11 | 0.12 |
| 33.39 | 0 | 0 |
| 33.4 | 0.12 | 0.12 |
| 33.5 | 0.1 | 0.12 |
| 33.6 | 0.12 | 0.12 |
| 33.7 | 0.1 | 0.12 |
| 33.8 | 0.11 | 0.12 |
| 33.9 | 0.11 | 0.11 |
| 34.0 | 0.15 | 0.16 |
| 34.1 | 0.1 | 0.12 |
| 34.2 | 0.11 | 0.12 |
| 34.3 | 0.11 | 0.12 |
| 34.4 | 0.11 | 0.12 |
| 34.5 | 0.13 | 0.13 |
| 34.6 | 0.12 | 0.12 |
| 34.7 | 0.11 | 0.12 |
| 34.8 | 0.11 | 0.13 |
| 34.89 | 0 | 0 |
| 34.9 | 0.1 | 0.12 |
| 35.0 | 0.17 | 0.16 |
| 35.1 | 0.13 | 0.12 |
| 35.2 | 0.11 | 0.13 |
| 35.3 | 0.11 | 0.12 |
| 35.4 | 0.11 | 0.13 |
| 35.5 | 0.11 | 0.12 |
| 35.6 | 0.11 | 0.12 |
| 35.7 | 0.12 | 0.13 |
| 35.8 | 0.11 | 0.12 |
| 35.9 | 0.11 | 0.12 |
| 36.0 | 0.15 | 0.16 |
| 36.1 | 0.12 | 0.12 |
| 36.2 | 0.11 | 0.12 |
| 36.3 | 0.1 | 0.12 |
| 36.4 | 0.12 | 0.12 |
| 36.5 | 0.11 | 0.13 |
| 36.6 | 0.11 | 0.13 |
| 36.7 | 0.12 | 0.13 |
| 36.78 | 0 | 0 |
| 36.8 | 0.1 | 0.13 |
| 36.88 | 0 | 0 |
| 36.9 | 0.12 | 0.13 |
| 36.94 | 0 | 0 |
| 37.0 | 0.17 | 0.17 |
| 37.1 | 0.12 | 0.12 |
| 37.2 | 0.11 | 0.13 |
| 37.3 | 0.13 | 0.12 |
| 37.4 | 0.12 | 0.12 |
| 37.5 | 0.11 | 0.12 |
| 37.6 | 0.11 | 0.13 |
| 37.63 | 0 | 0 |
| 37.7 | 0.1 | 0.13 |
| 37.73 | 0 | 0 |
| 37.8 | 0.11 | 0.13 |
| 37.9 | 0.12 | 0.13 |
| 38.0 | 0.16 | 0.17 |
| 38.1 | 0.11 | 0.12 |
| 38.2 | 0.14 | 0.13 |
| 38.3 | 0.12 | 0.13 |
| 38.4 | 0.12 | 0.13 |
| 38.5 | 0.12 | 0.13 |
| 38.6 | 0.13 | 0.13 |
| 38.7 | 0.11 | 0.13 |
| 38.77 | 0 | 0 |
| 38.8 | 0.14 | 0.13 |
| 38.9 | 0.12 | 0.13 |
| 39.0 | 0.18 | 0.17 |
| 39.1 | 0.12 | 0.12 |
| 39.2 | 0.12 | 0.13 |
| 39.3 | 0.12 | 0.13 |
| 39.4 | 0.12 | 0.13 |
| 39.5 | 0.12 | 0.12 |
| 39.6 | 0.12 | 0.13 |
| 39.7 | 0.13 | 0.12 |
| 39.8 | 0.11 | 0.12 |
| 39.9 | 0.12 | 0.12 |
| 39.95 | 0 | 0 |
| 40.0 | 0.17 | 0.17 |
| 40.1 | 0.12 | 0.13 |
| 40.2 | 0.11 | 0.13 |
| 40.3 | 0.11 | 0.12 |
| 40.4 | 0.12 | 0.12 |
| 40.5 | 0.13 | 0.13 |
| 40.6 | 0.12 | 0.12 |
| 40.7 | 0.14 | 0.13 |
| 40.8 | 0.12 | 0.13 |
| 40.9 | 0.12 | 0.13 |
| 41.0 | 0.18 | 0.17 |
| 41.1 | 0.12 | 0.13 |
| 41.2 | 0.12 | 0.13 |
| 41.3 | 0.13 | 0.13 |
| 41.4 | 0.12 | 0.13 |
| 41.5 | 0.13 | 0.12 |
| 41.6 | 0.11 | 0.13 |
| 41.7 | 0.13 | 0.13 |
| 41.8 | 0.12 | 0.12 |
| 41.85 | 0 | 0 |
| 41.9 | 0.12 | 0.12 |
| 42.0 | 0.2 | 0.18 |
| 42.1 | 0.13 | 0.13 |
| 42.2 | 0.11 | 0.12 |
| 42.3 | 0.12 | 0.13 |
| 42.4 | 0.11 | 0.13 |
| 42.5 | 0.13 | 0.13 |
| 42.6 | 0.13 | 0.13 |
| 42.7 | 0.14 | 0.13 |
| 42.8 | 0.13 | 0.13 |
| 42.9 | 0.13 | 0.13 |
| 43.0 | 0.2 | 0.18 |
| 43.1 | 0.13 | 0.13 |
| 43.2 | 0.13 | 0.13 |
| 43.3 | 0.13 | 0.12 |
| 43.38 | 0 | 0 |
| 43.4 | 0.12 | 0.13 |
| 43.5 | 0.14 | 0.13 |
| 43.6 | 0.12 | 0.13 |
| 43.61 | 0 | 0 |
| 43.7 | 0.13 | 0.13 |
| 43.8 | 0.12 | 0.13 |
| 43.9 | 0.13 | 0.13 |
| 44.0 | 0.21 | 0.18 |
| 44.1 | 0.12 | 0.13 |
| 44.2 | 0.14 | 0.13 |
| 44.3 | 0.13 | 0.14 |
| 44.4 | 0.12 | 0.13 |
| 44.5 | 0.12 | 0.13 |
| 44.6 | 0.12 | 0.13 |
| 44.7 | 0.12 | 0.13 |
| 44.8 | 0.14 | 0.13 |
| 44.9 | 0.13 | 0.13 |
| 45.0 | 0.19 | 0.18 |
| 45.1 | 0.13 | 0.13 |
| 45.2 | 0.14 | 0.13 |
| 45.3 | 0.14 | 0.13 |
| 45.4 | 0.12 | 0.13 |
| 45.5 | 0.13 | 0.14 |
| 45.6 | 0.13 | 0.12 |
| 45.7 | 0.13 | 0.13 |
| 45.8 | 0.13 | 0.13 |
| 45.9 | 0.14 | 0.13 |
| 46.0 | 0.19 | 0.18 |
| 46.1 | 0.13 | 0.13 |
| 46.2 | 0.13 | 0.14 |
| 46.3 | 0.14 | 0.13 |
| 46.4 | 0.12 | 0.13 |
| 46.5 | 0.12 | 0.13 |
| 46.6 | 0.13 | 0.14 |
| 46.7 | 0.14 | 0.14 |
| 46.74 | 0 | 0 |
| 46.8 | 0.13 | 0.13 |
| 46.9 | 0.13 | 0.13 |
| 47.0 | 0.2 | 0.18 |
| 47.1 | 0.15 | 0.13 |
| 47.2 | 0.14 | 0.14 |
| 47.3 | 0.13 | 0.14 |
| 47.36 | 0 | 0 |
| 47.4 | 0.13 | 0.13 |
| 47.5 | 0.13 | 0.13 |
| 47.6 | 0.14 | 0.13 |
| 47.7 | 0.14 | 0.14 |
| 47.8 | 0.13 | 0.14 |
| 47.9 | 0.13 | 0.13 |
| 48.0 | 0.22 | 0.19 |
| 48.1 | 0.14 | 0.13 |
| 48.2 | 0.15 | 0.13 |
| 48.3 | 0.13 | 0.14 |
| 48.4 | 0.14 | 0.14 |
| 48.5 | 0.14 | 0.13 |
| 48.6 | 0.14 | 0.13 |
| 48.7 | 0.13 | 0.13 |
| 48.8 | 0.13 | 0.13 |
| 48.9 | 0.14 | 0.14 |
| 49.0 | 0.22 | 0.18 |
| 49.1 | 0.13 | 0.14 |
| 49.2 | 0.13 | 0.13 |
| 49.3 | 0.13 | 0.13 |
| 49.4 | 0.14 | 0.14 |
| 49.5 | 0.14 | 0.14 |
| 49.6 | 0.14 | 0.14 |
| 49.63 | 0 | 0 |
| 49.69 | 0 | 0 |
| 49.7 | 0.14 | 0.13 |
| 49.8 | 0.14 | 0.13 |
| 49.9 | 0.14 | 0.14 |
| 50.0 | 0.21 | 0.19 |
| 50.1 | 0.13 | 0.14 |
| 50.2 | 0.13 | 0.13 |
| 50.3 | 0.14 | 0.13 |
| 50.4 | 0.13 | 0.13 |
| 50.5 | 0.14 | 0.13 |
| 50.6 | 0.13 | 0.13 |
| 50.7 | 0.14 | 0.14 |
| 50.8 | 0.13 | 0.13 |
| 50.9 | 0.14 | 0.14 |
| 51.0 | 0.21 | 0.18 |
| 51.1 | 0.15 | 0.13 |
| 51.2 | 0.13 | 0.13 |
| 51.3 | 0.12 | 0.13 |
| 51.4 | 0.13 | 0.14 |
| 51.5 | 0.13 | 0.14 |
| 51.6 | 0.14 | 0.13 |
| 51.7 | 0.14 | 0.14 |
| 51.8 | 0.13 | 0.13 |
| 51.9 | 0.14 | 0.13 |
| 52.0 | 0.23 | 0.18 |
| 52.1 | 0.14 | 0.13 |
| 52.2 | 0.14 | 0.14 |
| 52.3 | 0.15 | 0.13 |
| 52.4 | 0.13 | 0.14 |
| 52.5 | 0.14 | 0.13 |
| 52.58 | 0 | 0 |
| 52.6 | 0.14 | 0.14 |
| 52.7 | 0.15 | 0.13 |
| 52.8 | 0.14 | 0.14 |
| 52.9 | 0.13 | 0.14 |
| 53.0 | 0.22 | 0.19 |
| 53.1 | 0.14 | 0.14 |
| 53.2 | 0.14 | 0.13 |
| 53.3 | 0.14 | 0.14 |
| 53.4 | 0.14 | 0.13 |
| 53.5 | 0.15 | 0.14 |
| 53.6 | 0.14 | 0.14 |
| 53.7 | 0.14 | 0.13 |
| 53.8 | 0.15 | 0.13 |
| 53.9 | 0.14 | 0.13 |
| 54.0 | 0.23 | 0.19 |
| 54.1 | 0.15 | 0.13 |
| 54.2 | 0.14 | 0.14 |
| 54.22 | 0 | 0 |
| 54.3 | 0.16 | 0.13 |
| 54.4 | 0.14 | 0.13 |
| 54.5 | 0.13 | 0.14 |
| 54.6 | 0.13 | 0.14 |
| 54.7 | 0.14 | 0.14 |
| 54.8 | 0.14 | 0.13 |
| 54.9 | 0.13 | 0.14 |
| 55.0 | 0.22 | 0.18 |
| 55.1 | 0.14 | 0.14 |
| 55.2 | 0.14 | 0.14 |
| 55.3 | 0.14 | 0.13 |
| 55.4 | 0.14 | 0.14 |
| 55.5 | 0.14 | 0.14 |
| 55.6 | 0.14 | 0.13 |
| 55.7 | 0.14 | 0.13 |
| 55.8 | 0.13 | 0.13 |
| 55.9 | 0.14 | 0.13 |
| 56.0 | 0.22 | 0.18 |
| 56.1 | 0.14 | 0.13 |
| 56.2 | 0.13 | 0.14 |
| 56.26 | 0 | 0 |
| 56.3 | 0.14 | 0.14 |
| 56.4 | 0.13 | 0.14 |
| 56.5 | 0.14 | 0.13 |
| 56.6 | 0.15 | 0.13 |
| 56.7 | 0.15 | 0.13 |
| 56.8 | 0.15 | 0.13 |
| 56.9 | 0.13 | 0.14 |
| 57.0 | 0.23 | 0.19 |
| 57.1 | 0.14 | 0.13 |
| 57.2 | 0.16 | 0.13 |
| 57.3 | 0.13 | 0.14 |
| 57.4 | 0.15 | 0.13 |
| 57.5 | 0.14 | 0.14 |
| 57.56 | 0 | 0 |
| 57.6 | 0.14 | 0.13 |
| 57.7 | 0.15 | 0.13 |
| 57.8 | 0.15 | 0.13 |
| 57.9 | 0.14 | 0.13 |
| 58.0 | 0.22 | 0.19 |
| 58.1 | 0.14 | 0.13 |
| 58.19 | 0 | 0 |
| 58.2 | 0.14 | 0.13 |
| 58.3 | 0.15 | 0.13 |
| 58.4 | 0.14 | 0.13 |
| 58.5 | 0.13 | 0.13 |
| 58.6 | 0.14 | 0.13 |
| 58.7 | 0.16 | 0.13 |
| 58.77 | 0 | 0 |
| 58.8 | 0.14 | 0.13 |
| 58.9 | 0.15 | 0.13 |
| 59.0 | 0.23 | 0.19 |
| 59.1 | 0.15 | 0.13 |
| 59.2 | 0.14 | 0.13 |
| 59.3 | 0.14 | 0.13 |
| 59.4 | 0.14 | 0.13 |
| 59.5 | 0.14 | 0.14 |
| 59.6 | 0.14 | 0.13 |
| 59.7 | 0.14 | 0.13 |
| 59.8 | 0.14 | 0.13 |
| 59.9 | 0.15 | 0.13 |
| 60.0 | 0.21 | 0.19 |
| 60.1 | 0.14 | 0.13 |
| 60.2 | 0.15 | 0.14 |
| 60.3 | 0.15 | 0.12 |
| 60.4 | 0.15 | 0.14 |
| 60.5 | 0.14 | 0.13 |
| 60.6 | 0.14 | 0.12 |
| 60.69 | 0 | 0 |
| 60.7 | 0.13 | 0.14 |
| 60.8 | 0.14 | 0.13 |
| 60.9 | 0.13 | 0.12 |
| 61.0 | 0.23 | 0.18 |
| 61.1 | 0.14 | 0.13 |
| 61.2 | 0.15 | 0.13 |
| 61.3 | 0.14 | 0.13 |
| 61.4 | 0.13 | 0.13 |
| 61.5 | 0.13 | 0.13 |
| 61.6 | 0.15 | 0.13 |
| 61.7 | 0.14 | 0.13 |
| 61.8 | 0.12 | 0.13 |
| 61.9 | 0.15 | 0.12 |
| 62.0 | 0.22 | 0.18 |
| 62.1 | 0.14 | 0.12 |
| 62.2 | 0.14 | 0.13 |
| 62.3 | 0.14 | 0.13 |
| 62.31 | 0 | 0 |
| 62.4 | 0.14 | 0.13 |
| 62.5 | 0.13 | 0.12 |
| 62.6 | 0.13 | 0.13 |
| 62.7 | 0.14 | 0.13 |
| 62.8 | 0.14 | 0.13 |
| 62.9 | 0.14 | 0.13 |
| 63.0 | 0.21 | 0.18 |
| 63.1 | 0.13 | 0.13 |
| 63.2 | 0.14 | 0.13 |
| 63.3 | 0.13 | 0.12 |
| 63.4 | 0.15 | 0.12 |
| 63.5 | 0.14 | 0.12 |
| 63.6 | 0.13 | 0.13 |
| 63.7 | 0.13 | 0.13 |
| 63.8 | 0.13 | 0.12 |
| 63.9 | 0.14 | 0.13 |
| 64.0 | 0.21 | 0.17 |
| 64.1 | 0.14 | 0.12 |
| 64.2 | 0.13 | 0.12 |
| 64.3 | 0.13 | 0.13 |
| 64.4 | 0.15 | 0.12 |
| 64.5 | 0.14 | 0.12 |
| 64.6 | 0.14 | 0.13 |
| 64.7 | 0.13 | 0.12 |
| 64.8 | 0.13 | 0.12 |
| 64.9 | 0.13 | 0.12 |
| 65.0 | 0.23 | 0.17 |
| 65.1 | 0.13 | 0.12 |
| 65.2 | 0.13 | 0.13 |
| 65.3 | 0.13 | 0.13 |
| 65.4 | 0.13 | 0.12 |
| 65.5 | 0.14 | 0.12 |
| 65.6 | 0.14 | 0.12 |
| 65.7 | 0.14 | 0.12 |
| 65.8 | 0.13 | 0.12 |
| 65.9 | 0.13 | 0.12 |
| 66.0 | 0.21 | 0.17 |
| 66.1 | 0.13 | 0.13 |
| 66.2 | 0.14 | 0.12 |
| 66.3 | 0.14 | 0.12 |
| 66.4 | 0.11 | 0.12 |
| 66.5 | 0.13 | 0.12 |
| 66.6 | 0.14 | 0.12 |
| 66.7 | 0.14 | 0.13 |
| 66.8 | 0.14 | 0.12 |
| 66.9 | 0.13 | 0.12 |
| 67.0 | 0.21 | 0.16 |
| 67.1 | 0.14 | 0.12 |
| 67.2 | 0.14 | 0.12 |
| 67.3 | 0.14 | 0.13 |
| 67.4 | 0.14 | 0.12 |
| 67.5 | 0.14 | 0.12 |
| 67.6 | 0.14 | 0.12 |
| 67.7 | 0.12 | 0.12 |
| 67.8 | 0.14 | 0.12 |
| 67.9 | 0.13 | 0.12 |
| 68.0 | 0.22 | 0.17 |
| 68.1 | 0.13 | 0.12 |
| 68.2 | 0.12 | 0.12 |
| 68.3 | 0.12 | 0.11 |
| 68.4 | 0.14 | 0.12 |
| 68.5 | 0.13 | 0.12 |
| 68.6 | 0.13 | 0.12 |
| 68.7 | 0.14 | 0.12 |
| 68.8 | 0.13 | 0.12 |
| 68.9 | 0.14 | 0.12 |
| 69.0 | 0.2 | 0.16 |
| 69.1 | 0.13 | 0.12 |
| 69.14 | 0 | 0 |
| 69.2 | 0.13 | 0.12 |
| 69.3 | 0.12 | 0.12 |
| 69.4 | 0.15 | 0.12 |
| 69.5 | 0.13 | 0.12 |
| 69.6 | 0.13 | 0.12 |
| 69.7 | 0.12 | 0.11 |
| 69.8 | 0.11 | 0.12 |
| 69.9 | 0.13 | 0.12 |
| 69.98 | 0 | 0 |
| 70.0 | 0.2 | 0.16 |
| 70.1 | 0.13 | 0.11 |
| 70.2 | 0.13 | 0.12 |
| 70.26 | 0 | 0 |
| 70.3 | 0.12 | 0.12 |
| 70.4 | 0.12 | 0.11 |
| 70.5 | 0.13 | 0.11 |
| 70.6 | 0.13 | 0.12 |
| 70.7 | 0.12 | 0.12 |
| 70.8 | 0.13 | 0.12 |
| 70.9 | 0.12 | 0.12 |
| 70.94 | 0 | 0 |
| 71.0 | 0.19 | 0.16 |
| 71.1 | 0.13 | 0.11 |
| 71.2 | 0.13 | 0.11 |
| 71.3 | 0.11 | 0.11 |
| 71.4 | 0.11 | 0.12 |
| 71.5 | 0.12 | 0.11 |
| 71.6 | 0.13 | 0.12 |
| 71.7 | 0.13 | 0.11 |
| 71.8 | 0.14 | 0.12 |
| 71.9 | 0.14 | 0.11 |
| 72.0 | 0.2 | 0.16 |
| 72.1 | 0.11 | 0.12 |
| 72.2 | 0.13 | 0.11 |
| 72.3 | 0.14 | 0.11 |
| 72.4 | 0.12 | 0.11 |
| 72.5 | 0.11 | 0.11 |
| 72.6 | 0.13 | 0.11 |
| 72.7 | 0.12 | 0.11 |
| 72.8 | 0.14 | 0.11 |
| 72.9 | 0.12 | 0.11 |
| 73.0 | 0.21 | 0.16 |
| 73.1 | 0.11 | 0.11 |
| 73.2 | 0.11 | 0.11 |
| 73.3 | 0.13 | 0.1 |
| 73.4 | 0.11 | 0.12 |
| 73.5 | 0.12 | 0.11 |
| 73.6 | 0.13 | 0.11 |
| 73.7 | 0.11 | 0.11 |
| 73.8 | 0.12 | 0.11 |
| 73.9 | 0.11 | 0.11 |
| 74.0 | 0.2 | 0.15 |
| 74.1 | 0.12 | 0.11 |
| 74.2 | 0.11 | 0.11 |
| 74.3 | 0.12 | 0.11 |
| 74.4 | 0.12 | 0.11 |
| 74.5 | 0.12 | 0.11 |
| 74.6 | 0.11 | 0.1 |
| 74.7 | 0.11 | 0.1 |
| 74.8 | 0.12 | 0.1 |
| 74.9 | 0.12 | 0.1 |
| 75.0 | 0.2 | 0.15 |
| 75.1 | 0.11 | 0.1 |
| 75.2 | 0.12 | 0.1 |
| 75.3 | 0.12 | 0.1 |
| 75.4 | 0.13 | 0.1 |
| 75.5 | 0.12 | 0.1 |
| 75.6 | 0.11 | 0.1 |
| 75.7 | 0.12 | 0.11 |
| 75.8 | 0.12 | 0.1 |
| 75.9 | 0.11 | 0.11 |
| 76.0 | 0.19 | 0.15 |
| 76.1 | 0.11 | 0.1 |
| 76.2 | 0.12 | 0.1 |
| 76.3 | 0.11 | 0.1 |
| 76.4 | 0.12 | 0.1 |
| 76.5 | 0.1 | 0.11 |
| 76.6 | 0.12 | 0.11 |
| 76.7 | 0.1 | 0.1 |
| 76.8 | 0.11 | 0.1 |
| 76.9 | 0.12 | 0.1 |
| 77.0 | 0.18 | 0.15 |
| 77.1 | 0.12 | 0.1 |
| 77.2 | 0.11 | 0.1 |
| 77.3 | 0.11 | 0.1 |
| 77.4 | 0.12 | 0.1 |
| 77.5 | 0.11 | 0.1 |
| 77.6 | 0.12 | 0.1 |
| 77.63 | 0 | 0 |
| 77.7 | 0.11 | 0.1 |
| 77.8 | 0.11 | 0.09 |
| 77.9 | 0.12 | 0.1 |
| 78.0 | 0.19 | 0.14 |
| 78.1 | 0.11 | 0.1 |
| 78.2 | 0.11 | 0.1 |
| 78.3 | 0.11 | 0.1 |
| 78.4 | 0.12 | 0.1 |
| 78.5 | 0.1 | 0.1 |
| 78.6 | 0.1 | 0.1 |
| 78.7 | 0.11 | 0.09 |
| 78.8 | 0.12 | 0.1 |
| 78.9 | 0.1 | 0.09 |
| 79.0 | 0.19 | 0.14 |
| 79.1 | 0.11 | 0.09 |
| 79.2 | 0.11 | 0.1 |
| 79.3 | 0.1 | 0.1 |
| 79.4 | 0.11 | 0.1 |
| 79.5 | 0.1 | 0.09 |
| 79.6 | 0.09 | 0.1 |
| 79.7 | 0.11 | 0.1 |
| 79.8 | 0.12 | 0.09 |
| 79.9 | 0.12 | 0.1 |
| 80.0 | 0.19 | 0.13 |
| 80.1 | 0.09 | 0.09 |
| 80.2 | 0.11 | 0.1 |
| 80.3 | 0.1 | 0.09 |
| 80.4 | 0.1 | 0.09 |
| 80.5 | 0.1 | 0.09 |
| 80.6 | 0.11 | 0.09 |
| 80.7 | 0.1 | 0.09 |
| 80.8 | 0.1 | 0.09 |
| 80.9 | 0.09 | 0.09 |
| 81.0 | 0.17 | 0.13 |
| 81.1 | 0.11 | 0.09 |
| 81.2 | 0.09 | 0.09 |
| 81.3 | 0.09 | 0.09 |
| 81.31 | 0 | 0 |
| 81.4 | 0.1 | 0.09 |
| 81.5 | 0.1 | 0.09 |
| 81.6 | 0.1 | 0.09 |
| 81.7 | 0.11 | 0.08 |
| 81.8 | 0.1 | 0.08 |
| 81.9 | 0.11 | 0.09 |
| 82.0 | 0.17 | 0.13 |
| 82.1 | 0.1 | 0.09 |
| 82.2 | 0.1 | 0.09 |
| 82.3 | 0.11 | 0.09 |
| 82.4 | 0.1 | 0.09 |
| 82.5 | 0.1 | 0.09 |
| 82.6 | 0.1 | 0.09 |
| 82.7 | 0.09 | 0.09 |
| 82.8 | 0.1 | 0.09 |
| 82.9 | 0.09 | 0.09 |
| 83.0 | 0.17 | 0.13 |
| 83.1 | 0.09 | 0.09 |
| 83.2 | 0.1 | 0.08 |
| 83.3 | 0.11 | 0.09 |
| 83.4 | 0.1 | 0.09 |
| 83.5 | 0.1 | 0.08 |
| 83.6 | 0.09 | 0.09 |
| 83.7 | 0.1 | 0.09 |
| 83.8 | 0.1 | 0.09 |
| 83.9 | 0.1 | 0.08 |
| 84.0 | 0.16 | 0.13 |
| 84.1 | 0.1 | 0.08 |
| 84.2 | 0.09 | 0.08 |
| 84.3 | 0.1 | 0.08 |
| 84.4 | 0.09 | 0.08 |
| 84.5 | 0.09 | 0.09 |
| 84.6 | 0.09 | 0.09 |
| 84.7 | 0.09 | 0.08 |
| 84.8 | 0.1 | 0.08 |
| 84.9 | 0.09 | 0.08 |
| 85.0 | 0.15 | 0.12 |
| 85.1 | 0.1 | 0.08 |
| 85.2 | 0.09 | 0.08 |
| 85.3 | 0.1 | 0.08 |
| 85.4 | 0.09 | 0.08 |
| 85.5 | 0.08 | 0.08 |
| 85.6 | 0.09 | 0.08 |
| 85.7 | 0.08 | 0.08 |
| 85.8 | 0.09 | 0.08 |
| 85.9 | 0.08 | 0.08 |
| 86.0 | 0.14 | 0.11 |
| 86.1 | 0.08 | 0.08 |
| 86.2 | 0.08 | 0.08 |
| 86.3 | 0.08 | 0.07 |
| 86.4 | 0.09 | 0.08 |
| 86.5 | 0.09 | 0.08 |
| 86.6 | 0.08 | 0.08 |
| 86.7 | 0.09 | 0.08 |
| 86.8 | 0.08 | 0.08 |
| 86.9 | 0.08 | 0.08 |
| 87.0 | 0.15 | 0.11 |
| 87.1 | 0.09 | 0.07 |
| 87.2 | 0.09 | 0.07 |
| 87.3 | 0.08 | 0.08 |
| 87.4 | 0.09 | 0.07 |
| 87.5 | 0.09 | 0.07 |
| 87.6 | 0.09 | 0.07 |
| 87.7 | 0.08 | 0.08 |
| 87.8 | 0.08 | 0.07 |
| 87.9 | 0.09 | 0.07 |
| 88.0 | 0.14 | 0.11 |
| 88.1 | 0.08 | 0.07 |
| 88.2 | 0.07 | 0.07 |
| 88.3 | 0.09 | 0.07 |
| 88.4 | 0.07 | 0.07 |
| 88.48 | 0 | 0 |
| 88.5 | 0.08 | 0.07 |
| 88.6 | 0.08 | 0.07 |
| 88.7 | 0.08 | 0.07 |
| 88.8 | 0.09 | 0.07 |
| 88.9 | 0.07 | 0.07 |
| 89.0 | 0.13 | 0.1 |
| 89.1 | 0.08 | 0.07 |
| 89.2 | 0.08 | 0.07 |
| 89.3 | 0.1 | 0.07 |
| 89.4 | 0.09 | 0.07 |
| 89.5 | 0.09 | 0.07 |
| 89.6 | 0.08 | 0.07 |
| 89.7 | 0.07 | 0.06 |
| 89.8 | 0.08 | 0.06 |
| 89.9 | 0.08 | 0.07 |
| 90.0 | 0.12 | 0.1 |
| 90.1 | 0.07 | 0.07 |
| 90.2 | 0.07 | 0.07 |
| 90.3 | 0.08 | 0.07 |
| 90.4 | 0.07 | 0.06 |
| 90.5 | 0.07 | 0.06 |
| 90.6 | 0.08 | 0.06 |
| 90.7 | 0.08 | 0.06 |
| 90.8 | 0.08 | 0.07 |
| 90.9 | 0.07 | 0.07 |
| 91.0 | 0.14 | 0.09 |
| 91.1 | 0.08 | 0.06 |
| 91.2 | 0.07 | 0.06 |
| 91.3 | 0.08 | 0.06 |
| 91.4 | 0.06 | 0.06 |
| 91.5 | 0.07 | 0.06 |
| 91.6 | 0.07 | 0.06 |
| 91.7 | 0.08 | 0.06 |
| 91.8 | 0.07 | 0.06 |
| 91.9 | 0.07 | 0.06 |
| 92.0 | 0.13 | 0.09 |
| 92.1 | 0.07 | 0.06 |
| 92.2 | 0.08 | 0.06 |
| 92.3 | 0.07 | 0.06 |
| 92.4 | 0.07 | 0.06 |
| 92.5 | 0.07 | 0.06 |
| 92.6 | 0.07 | 0.06 |
| 92.7 | 0.08 | 0.06 |
| 92.8 | 0.07 | 0.07 |
| 92.9 | 0.07 | 0.06 |
| 93.0 | 0.11 | 0.09 |
| 93.1 | 0.07 | 0.06 |
| 93.2 | 0.07 | 0.06 |
| 93.3 | 0.07 | 0.06 |
| 93.4 | 0.07 | 0.06 |
| 93.5 | 0.07 | 0.06 |
| 93.6 | 0.07 | 0.06 |
| 93.7 | 0.07 | 0.06 |
| 93.8 | 0.06 | 0.06 |
| 93.9 | 0.07 | 0.06 |
| 94.0 | 0.11 | 0.08 |
| 94.1 | 0.07 | 0.06 |
| 94.2 | 0.07 | 0.06 |
| 94.3 | 0.07 | 0.06 |
| 94.4 | 0.06 | 0.06 |
| 94.46 | 0 | 0 |
| 94.5 | 0.07 | 0.05 |
| 94.6 | 0.07 | 0.06 |
| 94.7 | 0.07 | 0.06 |
| 94.8 | 0.07 | 0.05 |
| 94.9 | 0.06 | 0.06 |
| 95.0 | 0.1 | 0.08 |
| 95.1 | 0.06 | 0.05 |
| 95.2 | 0.07 | 0.06 |
| 95.3 | 0.06 | 0.06 |
| 95.4 | 0.06 | 0.05 |
| 95.5 | 0.06 | 0.05 |
| 95.6 | 0.05 | 0.05 |
| 95.7 | 0.06 | 0.05 |
| 95.8 | 0.06 | 0.05 |
| 95.9 | 0.06 | 0.05 |
| 96.0 | 0.1 | 0.07 |
| 96.1 | 0.06 | 0.05 |
| 96.2 | 0.06 | 0.05 |
| 96.3 | 0.06 | 0.05 |
| 96.4 | 0.06 | 0.05 |
| 96.5 | 0.07 | 0.05 |
| 96.6 | 0.06 | 0.05 |
| 96.7 | 0.05 | 0.05 |
| 96.8 | 0.06 | 0.05 |
| 96.9 | 0.06 | 0.05 |
| 97.0 | 0.09 | 0.07 |
| 97.1 | 0.06 | 0.05 |
| 97.2 | 0.06 | 0.05 |
| 97.3 | 0.06 | 0.05 |
| 97.4 | 0.06 | 0.05 |
| 97.5 | 0.05 | 0.05 |
| 97.6 | 0.06 | 0.05 |
| 97.7 | 0.06 | 0.04 |
| 97.8 | 0.06 | 0.05 |
| 97.9 | 0.06 | 0.04 |
| 98.0 | 0.08 | 0.06 |
| 98.1 | 0.05 | 0.04 |
| 98.2 | 0.05 | 0.04 |
| 98.3 | 0.05 | 0.04 |
| 98.4 | 0.05 | 0.04 |
| 98.5 | 0.05 | 0.04 |
| 98.6 | 0.05 | 0.04 |
| 98.7 | 0.05 | 0.04 |
| 98.8 | 0.05 | 0.04 |
| 98.9 | 0.04 | 0.04 |
| 99.0 | 0.08 | 0.05 |
| 99.1 | 0.04 | 0.04 |
| 99.2 | 0.04 | 0.03 |
| 99.3 | 0.04 | 0.03 |
| 99.4 | 0.04 | 0.03 |
| 99.5 | 0.04 | 0.03 |
| 99.6 | 0.03 | 0.03 |
| 99.7 | 0.03 | 0.03 |
| 99.8 | 0.03 | 0.02 |
| 99.9 | 0.03 | 0.02 |
| 100.0 | 0.05 | 0.03 |
| 100.1 | 0.02 | 0.02 |
| 100.2 | 0.02 | 0.02 |
| 100.3 | 0.02 | 0.01 |
| 100.4 | 0.02 | 0.01 |
| 100.5 | 0.02 | 0.02 |
| 100.6 | 0.02 | 0.01 |
| 100.7 | 0.02 | 0.01 |
| 100.8 | 0.02 | 0.01 |
| 100.9 | 0.01 | 0.01 |
| 101.0 | 0.03 | 0.02 |
| 101.1 | 0.01 | 0.01 |
| 101.2 | 0.01 | 0.01 |
| 101.3 | 0.01 | 0.01 |
| 101.4 | 0.01 | 0.01 |
| 101.5 | 0.01 | 0.01 |
| 101.6 | 0.01 | 0.01 |
| 101.7 | 0.01 | 0.01 |
| 101.8 | 0.01 | 0.01 |
| 101.9 | 0.01 | 0.01 |
| 102.0 | 0.02 | 0.01 |
| 102.1 | 0 | 0 |
| 102.2 | 0.01 | 0 |
| 102.3 | 0.01 | 0 |
| 102.4 | 0 | 0 |
| 102.5 | 0 | 0 |
| 102.6 | 0 | 0 |
| 102.7 | 0 | 0 |
| 102.8 | 0 | 0 |
| 102.9 | 0 | 0 |
| 103.0 | 0.01 | 0 |
| 103.1 | 0 | 0 |
| 103.2 | 0 | 0 |
| 103.3 | 0 | 0 |
| 103.4 | 0 | 0 |
| 103.5 | 0 | 0 |
| 103.6 | 0 | 0 |
| 103.7 | 0 | 0 |
| 103.8 | 0 | 0 |
| 103.9 | 0 | 0 |
| 104.0 | 0 | 0 |
| 104.1 | 0 | 0 |
| 104.2 | 0 | 0 |
| 104.3 | 0 | 0 |
| 104.4 | 0 | 0 |
| 104.5 | 0 | 0 |
| 104.6 | 0 | 0 |
| 104.7 | 0 | 0 |
| 104.8 | 0 | 0 |
| 104.9 | 0 | 0 |
| 105.0 | 0 | 0 |
| 105.1 | 0 | 0 |
| 105.2 | 0 | 0 |
| 105.3 | 0 | 0 |
| 105.4 | 0 | 0 |
| 105.5 | 0 | 0 |
| 105.6 | 0 | 0 |
| 105.7 | 0 | 0 |
| 105.8 | 0 | 0 |
| 105.9 | 0 | 0 |
| 106.0 | 0 | 0 |
| 106.1 | 0 | 0 |
| 106.2 | 0 | 0 |
| 106.3 | 0 | 0 |
| 106.4 | 0 | 0 |
| 106.5 | 0 | 0 |
| 106.6 | 0 | 0 |
| 106.7 | 0 | 0 |
| 106.8 | 0 | 0 |
| 106.9 | 0 | 0 |
| 107.0 | 0 | 0 |
| 107.1 | 0 | 0 |
| 107.2 | 0 | 0 |
| 107.3 | 0 | 0 |
| 107.4 | 0 | 0 |
| 107.5 | 0 | 0 |
| 107.6 | 0 | 0 |
| 107.7 | 0 | 0 |
| 107.8 | 0 | 0 |
| 107.9 | 0 | 0 |
| 108.0 | 0 | 0 |
| 108.1 | 0 | 0 |
| 108.2 | 0 | 0 |
| 108.3 | 0 | 0 |
| 108.4 | 0 | 0 |
| 108.5 | 0 | 0 |
| 108.6 | 0 | 0 |
| 108.7 | 0 | 0 |
| 108.8 | 0 | 0 |
| 108.9 | 0 | 0 |
| 109.0 | 0 | 0 |
| 109.1 | 0 | 0 |
| 109.2 | 0 | 0 |
| 109.3 | 0 | 0 |
| 109.4 | 0 | 0 |
| 109.5 | 0 | 0 |
| 109.6 | 0 | 0 |
| 109.7 | 0 | 0 |
| 109.8 | 0 | 0 |
| 109.9 | 0 | 0 |
| 110.0 | 0 | 0 |
| 110.1 | 0 | 0 |
| 110.2 | 0 | 0 |
| 110.3 | 0 | 0 |
| 110.4 | 0 | 0 |
| 110.5 | 0 | 0 |
| 110.6 | 0 | 0 |
| 110.7 | 0 | 0 |
| 110.8 | 0 | 0 |
| 110.9 | 0 | 0 |
| 111.0 | 0 | 0 |
| 111.1 | 0 | 0 |
| 111.2 | 0 | 0 |
| 111.3 | 0 | 0 |
| 111.4 | 0 | 0 |
| 111.5 | 0 | 0 |
| 111.6 | 0 | 0 |
| 111.7 | 0 | 0 |
| 111.8 | 0 | 0 |
| 111.9 | 0 | 0 |
| 112.0 | 0 | 0 |
| 112.1 | 0 | 0 |
| 112.2 | 0 | 0 |
| 112.3 | 0 | 0 |
| 112.4 | 0 | 0 |
| 112.5 | 0 | 0 |
| 112.6 | 0 | 0 |
| 112.7 | 0 | 0 |
| 112.8 | 0 | 0 |
| 112.9 | 0 | 0 |
| 113.0 | 0 | 0 |
| 113.1 | 0 | 0 |
| 113.2 | 0 | 0 |
| 113.3 | 0 | 0 |
| 113.4 | 0 | 0 |
| 113.5 | 0 | 0 |
| 113.6 | 0 | 0 |
| 113.7 | 0 | 0 |
| 113.8 | 0 | 0 |
| 113.9 | 0 | 0 |
| 114.0 | 0 | 0 |
| 114.1 | 0 | 0 |
| 114.2 | 0 | 0 |
| 114.4 | 0 | 0 |
| 114.5 | 0 | 0 |
| 114.6 | 0 | 0 |
| 114.7 | 0 | 0 |
| 114.8 | 0 | 0 |
| 114.9 | 0 | 0 |
| 115.0 | 0 | 0 |
| 115.1 | 0 | 0 |
| 115.3 | 0 | 0 |
| 115.4 | 0 | 0 |
| 115.5 | 0 | 0 |
| 115.6 | 0 | 0 |
| 115.7 | 0 | 0 |
| 115.8 | 0 | 0 |
| 116.0 | 0 | 0 |
| 116.1 | 0 | 0 |
| 116.2 | 0 | 0 |
| 116.3 | 0 | 0 |
| 116.4 | 0 | 0 |
| 116.5 | 0 | 0 |
| 116.8 | 0 | 0 |
| 117.1 | 0 | 0 |
| 117.2 | 0 | 0 |
| 117.4 | 0 | 0 |
| 117.5 | 0 | 0 |
| 117.6 | 0 | 0 |
| 117.7 | 0 | 0 |
| 117.9 | 0 | 0 |
| 118.0 | 0 | 0 |
| 118.1 | 0 | 0 |
| 118.2 | 0 | 0 |
| 118.4 | 0 | 0 |
| 118.6 | 0 | 0 |
| 118.9 | 0 | 0 |
| 119.0 | 0 | 0 |
| 119.1 | 0 | 0 |
| 119.2 | 0 | 0 |
| 119.4 | 0 | 0 |
| 119.6 | 0 | 0 |
| 119.7 | 0 | 0 |
| 120.0 | 0 | 0 |
| 120.2 | 0 | 0 |
| 120.5 | 0 | 0 |
| 120.8 | 0 | 0 |
| 120.9 | 0 | 0 |
| 121.0 | 0 | 0 |
| 121.1 | 0 | 0 |
| 121.4 | 0 | 0 |
| 121.7 | 0 | 0 |
| 122.1 | 0 | 0 |
| 122.2 | 0 | 0 |
| 122.4 | 0 | 0 |
| 122.5 | 0 | 0 |
| 122.7 | 0 | 0 |
| 122.8 | 0 | 0 |
| 123.0 | 0 | 0 |
| 123.2 | 0 | 0 |
| 123.3 | 0 | 0 |
| 123.5 | 0 | 0 |
| 123.9 | 0 | 0 |
| 124.5 | 0 | 0 |
| 124.7 | 0 | 0 |
| 125.0 | 0 | 0 |
| 125.2 | 0 | 0 |
| 125.3 | 0 | 0 |
| 125.7 | 0 | 0 |
| 126.1 | 0 | 0 |
| 126.2 | 0 | 0 |
| 126.3 | 0 | 0 |
| 126.4 | 0 | 0 |
| 127.0 | 0 | 0 |
| 127.1 | 0 | 0 |
| 127.3 | 0 | 0 |
| 127.4 | 0 | 0 |
| 127.6 | 0 | 0 |
| 128.0 | 0 | 0 |
| 128.1 | 0 | 0 |
| 128.6 | 0 | 0 |
| 128.7 | 0 | 0 |
| 129.4 | 0 | 0 |
| 129.5 | 0 | 0 |
| 129.6 | 0 | 0 |
| 130.1 | 0 | 0 |
| 130.2 | 0 | 0 |
| 130.4 | 0 | 0 |
| 131.0 | 0 | 0 |
| 131.8 | 0 | 0 |
| 132.1 | 0 | 0 |
| 132.2 | 0 | 0 |
| 132.5 | 0 | 0 |
| 132.7 | 0 | 0 |
| 132.9 | 0 | 0 |
| 133.1 | 0 | 0 |
| 134.1 | 0 | 0 |
| 134.3 | 0 | 0 |
| 134.5 | 0 | 0 |
| 136.0 | 0 | 0 |
| 136.1 | 0 | 0 |
| 136.7 | 0 | 0 |
| 137.2 | 0 | 0 |
| 137.8 | 0 | 0 |
| 137.9 | 0 | 0 |
| 138.9 | 0 | 0 |
| 139.7 | 0 | 0 |
| 140.4 | 0 | 0 |
| 141.1 | 0 | 0 |
| 143.6 | 0 | 0 |
| 145.0 | 0 | 0 |
| 145.8 | 0 | 0 |
| 146.1 | 0 | 0 |
| 148.0 | 0 | 0 |
| 150.7 | 0 | 0 |
| 152.5 | 0 | 0 |
| 152.6 | 0 | 0 |
| 152.7 | 0 | 0 |
| 153.0 | 0 | 0 |
| 153.7 | 0 | 0 |
| 154.9 | 0 | 0 |
| 155.3 | 0 | 0 |
| 156.3 | 0 | 0 |
| 162.0 | 0 | 0 |
| 165.8 | 0 | 0 |
| 172.0 | 0 | 0 |
| 177.7 | 0 | 0 |
| 180.3 | 0 | 0 |
| 182.8 | 0 | 0 |
| 184.6 | 0 | 0 |
| 193.0 | 0 | 0 |
| 366.6 | 0 | 0 |
| 892.3 | 0 | 0 |
Total Number of Credit Lines (Total Acc)¶
In [189]:
loan['total_acc'].isna().sum()
loan['total_acc'].fillna(0, inplace = True)
plot_var('total_acc', 'Total Number of Credit Lines', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Number of satisfactory accounts (Num sats)¶
In [190]:
loan['num_sats'].isna().sum()
loan['num_sats'].fillna(0, inplace = True)
The number of accounts on which the borrower is now delinquent. (Acc Now Delinq)¶
In [191]:
loan['acc_now_delinq'].isna().sum()
loan['acc_now_delinq'].fillna(0, inplace = True)
plot_var('acc_now_delinq', 'Number of satisfactory accounts', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Total current balance of all accounts (Total Cur Bal)¶
In [192]:
loan['tot_cur_bal'].isna().sum()
loan['tot_cur_bal'].fillna(0, inplace = True)
plot_var('tot_cur_bal', 'Total current balance of all accounts', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Total current balance of all installment accounts (Total Bal Il)¶
In [193]:
loan['total_bal_il'].isna().sum()
loan['total_bal_il'].fillna(0, inplace = True)
plot_var('total_bal_il', 'Total current balance of all installment accounts', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Ratio of total current balance to high credit/credit limit on all install acct (Il Util)¶
In [194]:
loan['il_util'].isna().sum()
loan['il_util'].fillna(0, inplace = True)
plot_var('il_util', 'High Credit/Credit Limit', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Maximum current balance owed on all revolving accounts (Max Bal BC)¶
In [195]:
loan['max_bal_bc'].isna().sum()
loan['max_bal_bc'].fillna(0, inplace = True)
plot_var('max_bal_bc', 'Maximum current balance owed on all revolving accounts', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Balance to credit limit on all trades (All Util)¶
In [196]:
loan['all_util'].isna().sum()
loan['all_util'].fillna(0, inplace = True)
plot_var('all_util', 'Balance to credit limit on all trades', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Total revolving high Limit (Total Rev Hi Lim)¶
In [197]:
loan['total_rev_hi_lim'].isna().sum()
loan['total_rev_hi_lim'].fillna(0, inplace = True)
plot_var('total_rev_hi_lim', 'Total revolving high Limit', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
In [198]:
loan.drop(['total_rev_hi_lim'],axis = 1, inplace = True)
Number of finance trades (Total Cu Tl)¶
In [199]:
loan['total_cu_tl'].isna().sum()
loan['total_cu_tl'].fillna(0, inplace = True)
plot_var('total_cu_tl', 'Number of finance trades', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Average current balance of all accounts (Avg Cur Bal)¶
In [200]:
loan['avg_cur_bal'].isna().sum()
loan['avg_cur_bal'].fillna(0, inplace = True)
plot_var('avg_cur_bal', 'Average current balance of all accounts', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Total open to buy on revolving bankcards. (Bc Open To Buy)¶
In [201]:
loan['bc_open_to_buy'].isna().sum()
loan['bc_open_to_buy'].fillna(0, inplace = True)
plot_var('bc_open_to_buy', 'Total open to buy on revolving bankcards', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Ratio of total current balance to high credit/credit limit for all bankcard accounts. (Bc Util)¶
In [202]:
loan['bc_util'].isna().sum()
loan['bc_util'].fillna(0, inplace = True)
plot_var('bc_util', 'High credit/Credit limit on all accounts', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
The past-due amount owed for the accounts on which the borrower is now delinquent. (Delinq Amnt)¶
In [203]:
loan['delinq_amnt'].isna().sum()
loan['delinq_amnt'].fillna(0, inplace = True)
plot_var('delinq_amnt', 'past-due amount owed for the delinquent accounts', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Mortgage accounts¶
In [204]:
loan['mort_acc'].isna().sum()
loan['mort_acc'].fillna(0, inplace = True)
Number of mortgage accounts. (Mort Acc)¶
In [205]:
loan.drop(['total_acc','total_bal_il','max_bal_bc','all_util',
'total_cu_tl'],1, inplace=True)
loan.shape
Out[205]:
(1283143, 91)
Indicadores de Crédito¶
In [206]:
matrix_corr(loan[['tot_hi_cred_lim','total_bal_ex_mort',
'total_bc_limit','total_il_high_credit_limit']]
, mirror=False)
Como se esta considerando la variable que incluye todos los saldos, esta variable tiene información redundante.
In [207]:
loan.drop(['total_bal_ex_mort'], axis = 1, inplace = True)
Como se puede observar, las variables recogidas bajo este epígrafe presentan una alta correlación, debido a que recogen información similar. Prescindiremos de la información redundante mediante la inclusión de una sola variable que aglutine toda la información.
Total high credit/credit limit¶
In [208]:
loan['tot_hi_cred_lim'].isnull().sum()
Out[208]:
70276
In [209]:
loan['tot_hi_cred_lim'].fillna(0, inplace = True)
In [210]:
plot_var('tot_hi_cred_lim', 'High credit/credit limit', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Prescindimos de esta variable por no tener poder discriminante.
In [211]:
loan.drop(['tot_hi_cred_lim'], axis = 1, inplace = True)
Total credit balance excluding mortgage¶
In [212]:
loan['total_bc_limit'].isna().sum()
Out[212]:
50030
In [213]:
loan['total_bc_limit'].fillna(0,inplace = True)
In [214]:
plot_var('total_bc_limit', 'Total credit balance w/o mortgage', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Total bankcard high credit/credit limit¶
In [215]:
loan['total_bc_limit'].isna().sum()
Out[215]:
0
In [216]:
loan['total_bc_limit'].fillna(0, inplace = True)
In [217]:
plot_var('total_bc_limit', 'Total bankcard high credit/credit limit', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
In [218]:
loan.drop(['total_bc_limit'],axis = 1, inplace = True)
Total installment high credit/credit limit¶
In [219]:
loan['total_il_high_credit_limit'].fillna(0, inplace = True)
In [220]:
plot_var('total_il_high_credit_limit', 'Total installment high credit/credit limit', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Rendimiento del Préstamo¶
Se trata de información del desempeño del préstamo y estos datos no se obtienen al entregar el préstamo sino después.
In [221]:
loan_perf_lst = ['out_prncp','out_prncp_inv','total_pymnt','total_pymnt_inv',
'total_rec_prncp','total_rec_int','total_rec_late_fee','recoveries',
'collection_recovery_fee','last_pymnt_d','last_pymnt_amnt',
'next_pymnt_d','last_credit_pull_d']
loan.drop(loan_perf_lst,1, inplace=True)
'Months since'¶
Bajo este epígrafe agrupamos todas las variables que hacen referencia al tiempo que ha pasado desde un evento determinado, ya que les daremos un tratamiento similar. Estas variables son:
- 'mths_since_recent_bc': months since most recent bankcard account opened.
- 'mths_since_recent_bc_dlq': months since most recent bankcard delinquency
- 'mths_since_recent_inq': months since most recent inquiry.
- 'mths_since_recent_revol_delinq': months since most recent revolving delinquency.
Calculamos la matriz de correlación. Observamos que existe una alta correlación entre 'mths_since_bc_dlq' y 'mths_since_recent_revol_delinq'. Dado que contienen información similar, prescindiremos de una de las variables.
In [222]:
matrix_corr(loan[['mths_since_recent_bc','mths_since_recent_bc_dlq',
'mths_since_recent_inq','mths_since_recent_revol_delinq']]
, mirror=False)
Months since most recent bankcard account opened.¶
Suponemos que los NA señalan las personas que no tienen ninguna cuenta abierta, por lo que les imputamos un 0.
In [223]:
loan['mths_since_recent_bc'].isna().sum()
Out[223]:
61975
In [224]:
loan['mths_since_recent_bc'].fillna(0, inplace = True)
plot_var('mths_since_recent_bc', 'Months since most recent bankcard account opened.', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
In [225]:
loan.drop(['mths_since_recent_bc'], axis=1, inplace = True)
Months since most recent bankcard delinquency¶
In [226]:
loan['mths_since_recent_bc_dlq'].isna().sum()
Out[226]:
979043
In [227]:
loan['mths_since_recent_bc_dlq'].fillna(0, inplace = True)
In [228]:
plot_var('mths_since_recent_bc_dlq', 'Months since most recent bankcard delinquency', continuous= True)
C:\Users\Marina\Anaconda3\lib\site-packages\seaborn\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code. warnings.warn(msg, UserWarning)
Se decide prescindir de esta variable.
In [229]:
loan.drop(['mths_since_recent_bc_dlq'], axis = 1, inplace = True)
Months since most recent inquiry.¶
Esta variable nos genera dudas sobre su relevancia. Una 'inquiry' es una comprobación que realiza Lending Club sobre las operaciones realizadas por el prestatario. Estas pueden ser 'soft' (en operaciones menores, que no afectan al credit score) o 'hard' (en operaciones de mayor riesgo, como hipotecas o líneas de crédito extra). Las soft inquiries no afectan al credit score. Por el contrario, una hard inquiry puede tener un mínimo impacto sobre el credit score del prestamista (1 a 5 puntos). Sólo la acumulación de hard inquiries tienen un impacto real. https://help.lendingclub.com/hc/en-us/articles/213803238-Understanding-your-credit-score Dado que esta variable no distingue entre soft y hard inquiries, consideramos que la inclusión de esta variable podría distorsionar el resultado, por lo que se decide prescindir de ella.
In [230]:
loan.drop(['mths_since_recent_inq'], axis=1, inplace = True)
Months since most recent revolving delinquency.¶
Prescindimos de esta variable ya que, como se avanzó anteriormente, está altamente relacionada con 'Months since most recent bankcard delinquency', variable más amplia que incluye esta información.
In [231]:
loan.drop(['mths_since_recent_revol_delinq'], axis=1, inplace = True)
Hardship plan¶
fuente: https://www.debt.org/credit/loans/personal/lending-club-review/
Lending Club ofrece a los usuarios con dificultades económicas un 'hardship plan' (plan de dificultades). Este plan consiste en el pago únicamente de los intereses devengados durante 3 meses. Nos planteamos si la concesión de un plan de dificultades, en algún momento (y no solo en la actualidad) es determinante o presenta una mayor incidencia en relación con la probabilidad de hacer un impago.
In [232]:
hardship = loan[['hardship_flag', 'hardship_type', 'hardship_reason', 'hardship_status',
'deferral_term', 'hardship_amount', 'hardship_start_date',
'hardship_end_date', 'payment_plan_start_date', 'hardship_length',
'hardship_dpd', 'hardship_loan_status',
'orig_projected_additional_accrued_interest',
'hardship_payoff_balance_amount']]
Hardship flag¶
'hardship_flag': factor que indica si el cliente tiene hardship plan en la actualidad. Sólo una persona tiene un plan de difultades en la actualidad, por lo que esta variable no presenta valor discriminante, y decidimos eliminarla.
In [233]:
hardship['hardship_flag'].value_counts()
Out[233]:
N 1283142 Y 1 Name: hardship_flag, dtype: int64
In [234]:
loan.drop(['hardship_flag'], axis=1, inplace = True)
In [235]:
loan.shape
Out[235]:
(1283143, 70)
Hardship type¶
Como anticipábamos en el epígrafe anterior, sólo existe un tipo de plan de contingencia (3 meses), por lo que utilizaremos esta variable como 'flag', para identificar qué usuario ha tenido concedido en algún momento un plan de dificultades. Como podemos observar, de los 1.306.387 casos totales, solamente 5.335 han esta condición. Decidimos imputar los valores ausentes con un string 'NO HARDSHIP PLAN' para evidenciar todavía más el valor dicotómico de esta variable, y convertimos en factor.
In [236]:
#solo existe un tipo de plan de contingencia: 3 meses.
loan['hardship_type'].value_counts()
Out[236]:
INTEREST ONLY-3 MONTHS DEFERRAL 5184 Name: hardship_type, dtype: int64
In [237]:
#Por exclusión, consideramos que las personas que no tienen un tipo de plan asignado, no tienen ninguno.
loan['hardship_type'].isna().sum()
Out[237]:
1277959
In [238]:
#Imputamos un string 'NO HARDSHIP PLAN' a cada NA
loan["hardship_type"].fillna("NO HARDSHIP PLAN", inplace = True)
In [239]:
plot_var('hardship_type', 'Hardship type', continuous= False)
In [240]:
#convertimos en factor
loan['hardship_type'] = loan.hardship_type.astype('category')
Decidimos eliminar el resto de columnas dentro de este epígrafe, bien porque contienen información repetida (length, por ejemplo, ya que anteriormente hemos establecido que sólo hay planes a tres meses) o no suficientemente relevante para incluir en el modelo.
In [241]:
loan.drop(['hardship_reason', 'hardship_status',
'deferral_term', 'hardship_amount', 'hardship_start_date',
'hardship_end_date', 'payment_plan_start_date', 'hardship_length',
'hardship_dpd', 'hardship_loan_status','hardship_last_payment_amount',
'orig_projected_additional_accrued_interest',
'hardship_payoff_balance_amount'], axis=1, inplace = True)
In [242]:
loan.shape
Out[242]:
(1283143, 57)
Disbursement method¶
Sólo encontramos una variable bajo este epígrafe, y hace referencia a la forma de recepción del préstamo: efectivo o transferencia bancaria. No estamos seguras de la relevancia de la variable, por lo que la incluimos inicialmente en el modelo, convirtiéndola en una variable categórica y evaluar posteriormente su impacto.
In [243]:
loan['disbursement_method'].value_counts()
Out[243]:
Cash 1277476 DirectPay 5667 Name: disbursement_method, dtype: int64
In [244]:
#comprobación de que no hay valores ausentes.
loan['disbursement_method'].isnull().sum()
Out[244]:
0
In [245]:
loan['disbursement_method']= loan.disbursement_method.astype('category')
Settlement¶
Este epígrafe hace referencia a las variables que analizan las variables relativas a 'settlement'. La liquidación de deudas es un acuerdo negociado en el que un prestamista acepta menos del monto adeudado, a veces significativamente menos, para liquidar legalmente una deuda.
Fuente: https://www.debt.org/settlement/ Dado que el inicio de este proceso implica haber incurrido en impago, de forma que el prestamista considera la deuda incobrable, decidimos prescindir de estas variables ya que, la información es redundante (la variable dependiente loan_Status ya contiene esta información y es la que tratamos de predecir). Información como la fecha de negociación o la cantidad que efectivamente se asume no es relevante.
In [246]:
settlement = loan[['debt_settlement_flag', 'debt_settlement_flag_date',
'settlement_status', 'settlement_date', 'settlement_amount',
'settlement_percentage', 'settlement_term']]
In [247]:
loan.drop(['debt_settlement_flag', 'debt_settlement_flag_date',
'settlement_status', 'settlement_date', 'settlement_amount',
'settlement_percentage', 'settlement_term'], axis=1, inplace = True)
Revisión del trabajo realizado.¶
Tras el trabajo de análisis exploratorio e ingeniería de datos realizado, procedemos a realizar una serie de comprobaciones posteriores, para verificar que el dataset tiene la forma deseada.
In [248]:
#Convertimos la variable objetivo en booleana
di = {"Default":1, "No Default":0}
loan = loan.replace({"credit_risk": di})
loan.drop('loan_status', axis=1, inplace=True)
In [249]:
for col in loan.select_dtypes(include=['object']).columns:
loan[col] = loan[col].astype('category')
Obtenemos las medidas básicas de cada variable, como medida adicional de control.
In [250]:
for col in loan.select_dtypes(include=['category']).columns:
print ("Column {} has {} unique instances and NaN {}%".format( col, len(loan[col].unique()),round(loan[col].isna().sum()/len(loan)*100,2)) )
Column home_ownership has 4 unique instances and NaN 0.0% Column verification_status has 2 unique instances and NaN 0.0% Column initial_list_status has 2 unique instances and NaN 0.0% Column hardship_type has 2 unique instances and NaN 0.0% Column disbursement_method has 2 unique instances and NaN 0.0% Column loan_amnt_range has 6 unique instances and NaN 0.0% Column amt_difference has 2 unique instances and NaN 0.0% Column purpose_g has 4 unique instances and NaN 0.0% Column region has 5 unique instances and NaN 0.0%
In [251]:
for col in loan.select_dtypes(include=['number']).columns:
print ("Column {}: Mean:{}, Min:{}, Max:{}, NaN: {}%".format( col,round(loan[col].mean(),2),round(loan[col].min(),2), round(loan[col].max(),2),round(loan[col].isna().sum()/len(loan)*100,2)) )
Column term: Mean:41.73, Min:36, Max:60, NaN: 0.0% Column int_rate: Mean:13.23, Min:5.31, Max:30.99, NaN: 0.0% Column installment: Mean:435.44, Min:4.93, Max:1719.83, NaN: 0.0% Column grade: Mean:5.25, Min:1, Max:7, NaN: 0.0% Column emp_length: Mean:5.65, Min:0.0, Max:10.0, NaN: 0.0% Column annual_inc: Mean:4.82, Min:0.0, Max:7.04, NaN: 0.0% Column dti: Mean:18.0, Min:-1.0, Max:49.96, NaN: 0.0% Column mths_since_last_delinq: Mean:17.0, Min:0.0, Max:226.0, NaN: 0.0% Column mths_since_last_record: Mean:11.96, Min:0.0, Max:129.0, NaN: 0.0% Column open_acc: Mean:11.6, Min:0.0, Max:90.0, NaN: 0.0% Column revol_util: Mean:51.93, Min:0.0, Max:892.3, NaN: 0.0% Column collections_12_mths_ex_med: Mean:0.02, Min:0.0, Max:20.0, NaN: 0.0% Column mths_since_last_major_derog: Mean:11.46, Min:0.0, Max:226.0, NaN: 0.0% Column acc_now_delinq: Mean:0.01, Min:0.0, Max:14.0, NaN: 0.0% Column tot_coll_amt: Mean:235.97, Min:0.0, Max:9152545.0, NaN: 0.0% Column tot_cur_bal: Mean:132662.51, Min:0.0, Max:8000078.0, NaN: 0.0% Column il_util: Mean:23.05, Min:0.0, Max:558.0, NaN: 0.0% Column acc_open_past_24mths: Mean:4.51, Min:0.0, Max:64.0, NaN: 0.0% Column avg_cur_bal: Mean:12663.02, Min:0.0, Max:958084.0, NaN: 0.0% Column bc_open_to_buy: Mean:9615.45, Min:0.0, Max:559912.0, NaN: 0.0% Column bc_util: Mean:57.16, Min:0.0, Max:339.6, NaN: 0.0% Column chargeoff_within_12_mths: Mean:0.01, Min:0.0, Max:10.0, NaN: 0.0% Column delinq_amnt: Mean:15.19, Min:0.0, Max:249925.0, NaN: 0.0% Column mo_sin_old_il_acct: Mean:115.42, Min:0.0, Max:999.0, NaN: 0.0% Column mo_sin_old_rev_tl_op: Mean:171.58, Min:0.0, Max:852.0, NaN: 0.0% Column mo_sin_rcnt_tl: Mean:7.41, Min:0.0, Max:314.0, NaN: 0.0% Column mort_acc: Mean:1.61, Min:0.0, Max:51.0, NaN: 0.0% Column num_accts_ever_120_pd: Mean:0.48, Min:0.0, Max:51.0, NaN: 0.0% Column num_sats: Mean:11.12, Min:0.0, Max:90.0, NaN: 0.0% Column num_tl_90g_dpd_24m: Mean:0.08, Min:0.0, Max:39.0, NaN: 0.0% Column percent_bc_gt_75: Mean:43.14, Min:0.0, Max:100.0, NaN: 0.0% Column tax_liens: Mean:0.05, Min:0.0, Max:85.0, NaN: 0.0% Column total_il_high_credit_limit: Mean:39617.76, Min:0.0, Max:2101913.0, NaN: 0.0% Column credit_risk: Mean:0.2, Min:0, Max:1, NaN: 0.0% Column issue_year: Mean:2014.87, Min:2007, Max:2018, NaN: 0.0% Column Age_Borrower: Mean:20.88, Min:-0.0, Max:85.76, NaN: 0.0% Column delinq_2yrs_cat: Mean:0.19, Min:0, Max:1, NaN: 0.0% Column pub_rec_cat: Mean:0.17, Min:0, Max:1, NaN: 0.0% Column acc_ratio: Mean:0.5, Min:0.0, Max:1.75, NaN: 0.0% Column revol_bal_cat: Mean:1.0, Min:0, Max:1, NaN: 0.0%
In [252]:
loan = pd.get_dummies(loan, columns=loan.select_dtypes(include=['category']).columns)
In [253]:
loan['credit_risk'].value_counts(normalize=True)
Out[253]:
0 0.799936 1 0.200064 Name: credit_risk, dtype: float64
In [254]:
loan.shape
Out[254]:
(1283143, 69)
Selección de Variables¶
In [255]:
# get non-none values for each col
var_n = pd.DataFrame(loan.count()).reset_index()
var_n.columns=['index', 'n']
# get correlation with binary outcome
var_cor = pd.DataFrame((loan.corr()['credit_risk'])).reset_index()
var_cor.columns=['index', 'correlation']
# get data type
var_type = pd.DataFrame(loan.dtypes).reset_index()
var_type.columns=['index', 'type']
var_type
# load data dictionary
#var_dict = pd.read_csv('H:\\lending-club-loan-data\\dictionary.csv', encoding = "ISO-8859-1")
#var_dict.columns = ['index','Description']
Out[255]:
| index | type | |
|---|---|---|
| 0 | term | int64 |
| 1 | int_rate | float64 |
| 2 | installment | float64 |
| 3 | grade | int64 |
| 4 | emp_length | float64 |
| 5 | annual_inc | float64 |
| 6 | dti | float64 |
| 7 | mths_since_last_delinq | float64 |
| 8 | mths_since_last_record | float64 |
| 9 | open_acc | float64 |
| 10 | revol_util | float64 |
| 11 | collections_12_mths_ex_med | float64 |
| 12 | mths_since_last_major_derog | float64 |
| 13 | acc_now_delinq | float64 |
| 14 | tot_coll_amt | float64 |
| 15 | tot_cur_bal | float64 |
| 16 | il_util | float64 |
| 17 | acc_open_past_24mths | float64 |
| 18 | avg_cur_bal | float64 |
| 19 | bc_open_to_buy | float64 |
| 20 | bc_util | float64 |
| 21 | chargeoff_within_12_mths | float64 |
| 22 | delinq_amnt | float64 |
| 23 | mo_sin_old_il_acct | float64 |
| 24 | mo_sin_old_rev_tl_op | float64 |
| 25 | mo_sin_rcnt_tl | float64 |
| 26 | mort_acc | float64 |
| 27 | num_accts_ever_120_pd | float64 |
| 28 | num_sats | float64 |
| 29 | num_tl_90g_dpd_24m | float64 |
| ... | ... | ... |
| 39 | revol_bal_cat | int64 |
| 40 | home_ownership_MORTGAGE | uint8 |
| 41 | home_ownership_OTHER | uint8 |
| 42 | home_ownership_OWN | uint8 |
| 43 | home_ownership_RENT | uint8 |
| 44 | verification_status_1 | uint8 |
| 45 | verification_status_Not Verified | uint8 |
| 46 | initial_list_status_f | uint8 |
| 47 | initial_list_status_w | uint8 |
| 48 | hardship_type_INTEREST ONLY-3 MONTHS DEFERRAL | uint8 |
| 49 | hardship_type_NO HARDSHIP PLAN | uint8 |
| 50 | disbursement_method_Cash | uint8 |
| 51 | disbursement_method_DirectPay | uint8 |
| 52 | loan_amnt_range_0-5000 | uint8 |
| 53 | loan_amnt_range_5000-10000 | uint8 |
| 54 | loan_amnt_range_10000-15000 | uint8 |
| 55 | loan_amnt_range_15000-20000 | uint8 |
| 56 | loan_amnt_range_20000-25000 | uint8 |
| 57 | loan_amnt_range_25000+ | uint8 |
| 58 | amt_difference_Igual | uint8 |
| 59 | amt_difference_Menos | uint8 |
| 60 | purpose_g_debt | uint8 |
| 61 | purpose_g_life_event | uint8 |
| 62 | purpose_g_major_purchase | uint8 |
| 63 | purpose_g_other | uint8 |
| 64 | region_MidWest | uint8 |
| 65 | region_NorthEast | uint8 |
| 66 | region_SouthEast | uint8 |
| 67 | region_SouthWest | uint8 |
| 68 | region_West | uint8 |
69 rows × 2 columns
In [256]:
import scipy
In [257]:
def do_test(x):
# do t test for continuous var
if (x.dtypes == 'float64') | (x.dtypes == 'int64'):
group1, group2 = [g[1] for g in x.groupby(loan['credit_risk'])]
t, p = scipy.stats.ttest_ind(group1, group2,nan_policy='omit')
if np.isnan(np.ma.getdata(t)):
p=1
return p
# do chi squared test for categorical var
if x.dtypes == 'object':
observed = pd.crosstab(x, loan['credit_risk'])
chi, p = scipy.stats.chi2_contingency(observed, correction=False)[0:2]
return p
In [258]:
# calculate p values for all variables
pval = pd.DataFrame(loan.apply(do_test)).reset_index()
pval.columns=['index', 'p']
In [259]:
var_n = pd.DataFrame(loan.count()).reset_index()
var_n.columns=['index', 'n']
# get correlation with binary outcome
var_cor = pd.DataFrame((loan.corr()['credit_risk'])).reset_index()
var_cor.columns=['index', 'correlation']
# get data type
var_type = pd.DataFrame(loan.dtypes).reset_index()
var_type.columns=['index', 'type']
In [260]:
var_n.shape
Out[260]:
(69, 2)
In [261]:
# merger p value and variable info together and sort
var_info = pd.merge(var_n, pval, how='left', on=['index'])
var_info = pd.merge(var_info, var_type, how='left', on=['index'])
#var_info = pd.merge(var_info, var_dict, how='left', on=['index'])
var_select = var_info.loc[((var_info['n']/len(loan)>.95)) & (var_info['p'] < 0.05/len(var_info))]
var_select.to_csv('selected_vars.csv')
print('Total number of variables selected =',len(var_select))
print('Type of variables:')
print(var_select['type'].value_counts())
plt.plot(var_select['p'],'ro')
var_select.sort_values(by=['index'])
Total number of variables selected = 61 Type of variables: float64 31 uint8 23 int64 7 Name: type, dtype: int64
Out[261]:
| index | n | p | type | |
|---|---|---|---|---|
| 35 | Age_Borrower | 1283143 | 0.000000e+00 | float64 |
| 13 | acc_now_delinq | 1283143 | 4.104544e-06 | float64 |
| 17 | acc_open_past_24mths | 1283143 | 0.000000e+00 | float64 |
| 38 | acc_ratio | 1283143 | 0.000000e+00 | float64 |
| 58 | amt_difference_Igual | 1283143 | 1.726919e-23 | uint8 |
| 59 | amt_difference_Menos | 1283143 | 1.726919e-23 | uint8 |
| 5 | annual_inc | 1283143 | 0.000000e+00 | float64 |
| 18 | avg_cur_bal | 1283143 | 0.000000e+00 | float64 |
| 19 | bc_open_to_buy | 1283143 | 0.000000e+00 | float64 |
| 20 | bc_util | 1283143 | 0.000000e+00 | float64 |
| 21 | chargeoff_within_12_mths | 1283143 | 7.062587e-04 | float64 |
| 11 | collections_12_mths_ex_med | 1283143 | 7.036485e-75 | float64 |
| 33 | credit_risk | 1283143 | 0.000000e+00 | int64 |
| 36 | delinq_2yrs_cat | 1283143 | 6.032415e-99 | int64 |
| 6 | dti | 1283143 | 0.000000e+00 | float64 |
| 4 | emp_length | 1283143 | 1.138772e-204 | float64 |
| 3 | grade | 1283143 | 0.000000e+00 | int64 |
| 48 | hardship_type_INTEREST ONLY-3 MONTHS DEFERRAL | 1283143 | 0.000000e+00 | uint8 |
| 49 | hardship_type_NO HARDSHIP PLAN | 1283143 | 0.000000e+00 | uint8 |
| 40 | home_ownership_MORTGAGE | 1283143 | 0.000000e+00 | uint8 |
| 42 | home_ownership_OWN | 1283143 | 3.729132e-09 | uint8 |
| 43 | home_ownership_RENT | 1283143 | 0.000000e+00 | uint8 |
| 16 | il_util | 1283143 | 0.000000e+00 | float64 |
| 46 | initial_list_status_f | 1283143 | 1.537279e-19 | uint8 |
| 47 | initial_list_status_w | 1283143 | 1.537279e-19 | uint8 |
| 2 | installment | 1283143 | 0.000000e+00 | float64 |
| 1 | int_rate | 1283143 | 0.000000e+00 | float64 |
| 34 | issue_year | 1283143 | 0.000000e+00 | int64 |
| 52 | loan_amnt_range_0-5000 | 1283143 | 0.000000e+00 | uint8 |
| 54 | loan_amnt_range_10000-15000 | 1283143 | 7.479078e-34 | uint8 |
| ... | ... | ... | ... | ... |
| 56 | loan_amnt_range_20000-25000 | 1283143 | 2.589719e-155 | uint8 |
| 57 | loan_amnt_range_25000+ | 1283143 | 0.000000e+00 | uint8 |
| 53 | loan_amnt_range_5000-10000 | 1283143 | 0.000000e+00 | uint8 |
| 23 | mo_sin_old_il_acct | 1283143 | 2.374473e-34 | float64 |
| 24 | mo_sin_old_rev_tl_op | 1283143 | 0.000000e+00 | float64 |
| 25 | mo_sin_rcnt_tl | 1283143 | 0.000000e+00 | float64 |
| 26 | mort_acc | 1283143 | 0.000000e+00 | float64 |
| 7 | mths_since_last_delinq | 1283143 | 2.247180e-16 | float64 |
| 12 | mths_since_last_major_derog | 1283143 | 1.239574e-162 | float64 |
| 8 | mths_since_last_record | 1283143 | 4.409626e-261 | float64 |
| 27 | num_accts_ever_120_pd | 1283143 | 2.071184e-46 | float64 |
| 28 | num_sats | 1283143 | 0.000000e+00 | float64 |
| 29 | num_tl_90g_dpd_24m | 1283143 | 6.217691e-37 | float64 |
| 9 | open_acc | 1283143 | 9.180317e-250 | float64 |
| 30 | percent_bc_gt_75 | 1283143 | 0.000000e+00 | float64 |
| 37 | pub_rec_cat | 1283143 | 1.038922e-282 | int64 |
| 61 | purpose_g_life_event | 1283143 | 6.509064e-113 | uint8 |
| 62 | purpose_g_major_purchase | 1283143 | 1.129928e-88 | uint8 |
| 63 | purpose_g_other | 1283143 | 1.994519e-14 | uint8 |
| 65 | region_NorthEast | 1283143 | 2.575000e-24 | uint8 |
| 66 | region_SouthEast | 1283143 | 1.385057e-29 | uint8 |
| 68 | region_West | 1283143 | 2.440081e-91 | uint8 |
| 39 | revol_bal_cat | 1283143 | 6.863130e-07 | int64 |
| 10 | revol_util | 1283143 | 0.000000e+00 | float64 |
| 31 | tax_liens | 1283143 | 1.855952e-30 | float64 |
| 0 | term | 1283143 | 0.000000e+00 | int64 |
| 15 | tot_cur_bal | 1283143 | 0.000000e+00 | float64 |
| 32 | total_il_high_credit_limit | 1283143 | 7.856516e-10 | float64 |
| 44 | verification_status_1 | 1283143 | 0.000000e+00 | uint8 |
| 45 | verification_status_Not Verified | 1283143 | 0.000000e+00 | uint8 |
61 rows × 4 columns
In [262]:
#Solo las variables seleccionadas
drop_list = [col for col in loan.columns if col not in var_select['index'].tolist()]
drop_list
loan_select =loan.drop(labels=drop_list, axis=1)
Definimos una función para comprobar que efectivamente no quedan NAs que puedan dificultar la fase de modelaje y lo aplicamos al dataset:
In [263]:
# Define a function to visulize the features with missing values, and % of total values, & datatype
def missing_values_table(df):
# Total missing values
mis_val = df.isnull().sum()
# Percentage of missing values
mis_val_percent = 100 * df.isnull().sum() / len(df)
mis_val_type = df.dtypes
# Make a table with the results
mis_val_table = pd.concat([mis_val, mis_val_percent, mis_val_type], axis=1)
# Rename the columns
mis_val_table_ren_columns = mis_val_table.rename(columns = {0 : 'Missing Values', 1 : '% of Total Values', 2: 'type'})
# Sort the table by percentage of missing descending
mis_val_table_ren_columns = mis_val_table_ren_columns[ mis_val_table_ren_columns.iloc[:,1] != 0].sort_values('% of Total Values', ascending=False).round(1)
# Print some summary information
print ("Your selected dataframe has " + str(df.shape[1]) + " columns.\n" "There are " + str(mis_val_table_ren_columns.shape[0]) + " columns that have missing values.")
# Return the dataframe with missing information
return mis_val_table_ren_columns
In [264]:
missing_values_table(loan)
Your selected dataframe has 69 columns. There are 0 columns that have missing values.
Out[264]:
| Missing Values | % of Total Values | type |
|---|
In [265]:
loan_select.to_csv('../data/2_EDAoutput.gz', compression='gzip')